INTRODUCTION

It has long been proposed that the tuning of sensory neurons is determined by adaptation to the statistics of the signals they need to encode (1, 2). In the visual domain, this notion has given rise to two broad families of unsupervised learning algorithms: those relying on the spatial structure of natural images, referred to as unsupervised spatial learning (USL) models (16), and those leveraging the spatiotemporal structure of natural image sequences, referred to as unsupervised temporal learning (UTL) models (715). Both kinds of learning have been applied to explain the ability of visual cortical representations to selectively code for the identity of visual objects, a property known as shape tuning, while tolerating variations in their appearance (e.g., because of position changes), a property known as transformation tolerance (or invariance) (16). These properties are built incrementally along the ventral stream (the cortical hierarchy devoted to shape processing), but the earliest evidence of shape tuning and invariance in the visual system can be traced back to primary visual cortex (V1), where simple cells first exhibit tuning for nontrivial geometrical patterns (oriented edges) and complex cells first display some degree of position tolerance (17).

In sparse coding theories (arguably the most popular incarnation of USL), maximizing the sparsity of the representation of natural images produces Gabor-like edge detectors that closely resemble the receptive fields (RFs) of V1 simple cells (5, 6). Other USL models, by optimizing objective functions that depend on the combination of several linear spatial filters, also account for the emergence of position-tolerant edge detectors, such as V1 complex cells (3, 4). The latter, however, have been more commonly modeled as the result of UTL, where the natural tendency of different object views to occur nearby in time is used to factor out object identity from other faster-varying, lower-level visual attributes. While some UTL models presuppose the existence of a bank of simple cells, upon which the complex cells representation is learned (7, 1115), other models, such as slow feature analysis (SFA), directly evolve complex cells from the pixel (i.e., retinal) representation, thus simultaneously learning shape selectivity and invariance (8, 9).

To date, it remains unclear what role these hypothesized learning mechanisms play in the developing visual cortex, despite the influence that early visual experience is known to exert on cortical tuning. This is demonstrated (e.g.) by the impact of monocular deprivation on the development of ocular dominance (18, 19), by the bias in orientation tuning produced by restricting early visual experience to a single orientation (20, 21), and by the need, for ferret visual cortex, to experience visual motion to develop direction selectivity (22). However, none of these manipulations was designed to specifically test the role of USL and/or UTL in mediating the development of simple and complex cells. As a result, empirical support for the role of sparse coding in determining orientation selectivity is still indirect (23, 6), as no study has succeeded in abolishing (or at least interfering with) the development of simple cells with Gabor-like tuning through manipulations of the visual environment (24). Similarly, no clean causal evidence has been gathered yet to demonstrate the involvement of UTL in postnatal development of invariance and/or selectivity in visual cortex. The only experiments suggesting the involvement of UTL in fostering invariant visual object representations during development come from behavioral studies of chicks object vision (25). In mammals, a few studies based on strobe rearing did investigate the effect of degrading the temporal continuity of the visual input on the developing cortex (2630), but they did not quantitatively probe whether this manipulation led to a reduction of invariance (see Discussion). More critically, strobe rearing does not allow effectively and selectively altering the temporal statistics of the visual input while sparing the spatial statistics (or vice versa). Short light flashes (10 s) also severely limit the experience with the spatial content of the visual input, as well as the overall amount of light exposure during development, especially when combined with low strobe rates (0.5 to 2 Hz). Conversely, higher strobe rates (8 Hz) allow still experiencing a strongly correlated visual input over time, given the dense, ordered sampling of the visual space performed by the visual system across consecutive flashes. This makes it impossible to disentangle the contribution of USL, UTL, or simpler light-dependent plasticity processes to the changes of orientation and/or direction tuning reported in some of these studies. In summary, the lack of conclusive evidence about the involvement of spatial and temporal learning processes in cortical development of selectivity and invariance calls for new studies based on tighter, better controlled manipulations of visual experience during postnatal development.

Our study was designed to causally test the involvement of UTL in the development of shape selectivity and transformation tolerance (i.e., simple and complex cells) in V1. To this aim, we took 18 newborn rats (housed in light-proof cabinets from birth) and, from postnatal day 14 (P14) [i.e., at eye opening (EO)] to P60 [i.e., well beyond the end of the critical period (31)], subjected them to daily, 4-hour long exposures inside an immersive visual environment. This consisted of a rectangular, transparent basin, surrounded on each side by a computer-controlled liquid crystal display (LCD) monitor, and placed inside a light-proof cabinet (fig. S1). Eight animals (the control group) were exposed to a battery of 16 natural movies (lasting from a few minutes to half an hour), while the remaining 10 rats (the experimental group) were exposed to their frame-scrambled versions (Fig. 1A). As a result of the scrambling, the correlation between the frames of a movie as a function of their temporal separation was close to zero at all tested time lags, while the image frames of the original movies remained strongly correlated over several seconds (compare the orange versus blue curves in Fig. 1, B and C; the average time constants of the exponential fits to the correlation functions were 6.9 1.3 ms and 1.47 0.10 s, respectively, for the frame-scrambled and original movies; see Fig. 1C, right). All movies were played at 15 Hz, which is approximately half of the critical flicker fusion frequency (~30 to 40 Hz) of the rat (32). This ensured that, while the temporal correlation of the input was substantially broken, no fusion occurred between consecutive frames of a movie, thus allowing the rats of the experimental group to fully experience the spatial content of the individual image frames. This likely enabled the experimental rats to also experience some amount of continuous transformation (e.g., translation) of the image frames, as the result of spontaneous head or eye movements during the 66.7-ms presentation time of each frame. This, along with the presence of some stable visual features in the physical environment (e.g., the dark edges of the monitors) and the possibility for the rats to see parts of their own body, allowed for some residual amount of temporal continuity in the visual experience of the experimental group. This incomplete disruption of temporal continuity was unavoidable, given the constraints of (i) granting the animals full access to the spatial content of natural visual scenes and (ii) trying to foster visual cortical development and plasticity by leaving the rats free to actively explore the environment (33), thus avoiding body restraint and head fixation. Crucially, despite these constraints, the temporal statistics of the visual stream experienced by the two groups of animals at time scales larger than 66.7 ms was radically different (Fig. 1, B and C), while the spatial statistics and overall amount of light exposure were very well matched. This allowed isolating the contribution of temporal contiguity to the postnatal development of V1 simple and complex cells.

(A) Two groups of rats, control (top) and experimental (bottom), were born in dark and housed in lightproof cabinets until EO (black bars). Afterward, the control rats were subjected to daily 4-hour-long exposures to natural videos inside the virtual cages (blue bar), while the experimental rats were subjected to the frame-scrambled versions of the same movies (orange bar). Starting from P60, neuronal recordings from V1 of both the control and experimental rats were performed under anesthesia (gray bars), while the animals were exposed to drifting gratings and movies of spatially and temporally correlated noise. (B) Left: The mean correlation between the image frames of one of the natural movies [same as in (A)] is plotted as a function of their temporal lag (blue curve). The dashed line shows the best exponential fit to the resulting autocorrelation function ( is the time constant of the fit). Right: The autocorrelation function obtained for the frame-scrambled version of the movie (orange curve) is shown along with its best exponential fit (dashed line). (C) Left: The autocorrelation functions of all the natural movies used during postnatal rearing of the control rats (blue curves) are shown along with the autocorrelation functions of their frame-scrambled versions (orange curves) used during postnatal rearing of the experimental rats. Right: The time constants of the best exponential fits to the autocorrelation functions of the natural movies (blue dots) and their frame-scrambled versions (orange dots) were significantly different (P < 0.001, one-tailed, unpaired t test). Photo credit: Giulio Matteucci and Mattia DAndola, SISSA (Trieste, Italy).

Shortly after the end of the controlled-rearing period, we performed multichannel extracellular recordings from V1 of each rat under fentanyl/medetomidin anesthesia (see Materials and Methods for details) (34). Our recordings mainly targeted layer 5, where complex cells are known to be more abundant (35), and layer 4, with the distributions of recorded units across the cortical depth and the cortical laminae being statistically the same for the control and experimental groups (fig. S2). During a recording session, each animal was presented with drifting gratings spanning 12 directions (from 0 to 330 in steps of 30) and with contrast-modulated movies of spatially and temporally correlated noise (34, 35). Responses to the noise movies allowed inferring the linear RF structure of the recorded units using the spike-triggered average (STA) analysis and the temporal scale over which the stimulus representation unfolded (see Materials and Methods). Responses to the drifting gratings were used to estimate the tuning of the neurons with the standard orientation selectivity index (OSI) and direction selectivity index (DSI) (defined in Materials and Methods) and to probe their sensitivity to phase shifts of their preferred gratings, thus measuring their position tolerance (see Discussion) (34, 35).

This is illustrated in Fig. 2A, which shows a representative complex cell from the control group (left, blue lines) and a representative simple cell from the experimental group (right, orange lines). Both units displayed sharp orientation tuning (polar plots), but the STA method successfully recovered a sharp, Gabor-like RF only for the simple cellas expected, given the nonlinear stimulus-response relationship of complex cells (34). Consistently, the response of the complex cell was only weakly modulated at the temporal frequency (4 Hz) of its preferred grating (middle plots), with the highest power spectral density concentrated at frequencies of <4 Hz (bottom plot). By contrast, the response of the simple cell was strongly phase modulated, with a power spectrum narrowly peaked at the grating frequency. Thus, by z-scoring the power spectral density of the response at the preferred grating frequency, it was possible to define a modulation index (MI) that distinguished between complex (MI < 3) and simple (MI > 3) cells (see Materials and Methods) (34, 36).

(A) A representative V1 complex cell of the control group (left, blue lines) is compared to a representative simple cell of the experimental group (right, orange lines). For each neuron, the graph shows, from top/left to bottom, (i) the linear RF structure inferred through STA, (ii) the direction tuning curve, (iii) the raster plot with the number of spikes (dots) fired across repeated presentations of the most effective grating stimulus, (iv) the corresponding peristimulus time histogram (PSTH) computed in 10-ms-wide time bins, and (v) its power spectrum with its mean (dotted line), its mean + SD (dashed line), and the 4-Hz frequency of the grating stimulus (vertical line) indicated. (B) Left: Distributions of the MI used to distinguish the poorly phase-modulated complex cells (MI < 3; gray-shaded area) from the strongly modulated simple cells (MI > 3), as obtained for the control (blue; n = 50) and experimental (orange; n = 75) V1 populations (only units with an OSI of >0.4 included). Both the distributions and their medians (dashed lines) were significantly different (P < 0.02, Kolmogorov-Smirnov test; ***P < 0.001, Wilcoxon test). Right: The fraction of units that were classified as complex cells (i.e., with an MI of <3) was significantly larger for the control than for the experimental group (***P < 0.001, Fishers exact test). (C) Left: Distributions of the orientation selectivity index (OSI), as obtained for the control (blue; n = 105) and experimental (orange; n = 158) V1 populations. No significant difference was found between the two distributions and their medians (P > 0.05, Kolmogorov-Smirnov test; P > 0.05, Wilcoxon test). Right: The fraction of sharply orientation-tuned units (i.e., units with an OSI of >0.6) did not differ between the two groups (P > 0.05, Fishers exact test). n.s., not significant.

We applied this criterion to the neuronal populations of 105 and 158 well-isolated single units recorded from, respectively, the control and experimental group, and we found a significantly lower fraction of complex cells in the latter (39%, 61 of 158) with respect to the former (55%, 58 of 105; P < 0.01, Fishers exact test). Consistently, the median MI for the control population (2.69 0.29) was significantly smaller than for the experimental one (3.52 0.25; P < 0.05, Wilcoxon test). Such a difference became very sharp after restricting the comparison to the neurons that, in both populations, were at least moderately orientation tuned (i.e., 50 control and 75 experimental units with an OSI of >0.4). The resulting MI distribution for the control group had a typical double-peak shape (34), featuring two maxima, at MI ~ 2 and MI ~ 5, corresponding to the two classes of the complex and simple cells (Fig. 2B, blue curve). Instead, for the experimental group, the peak at low MI was flattened out, leaving a single, prominent peak at MI ~ 5 (orange curve). This resulted in a large, significant difference between the two distributions and their medians (dashed lines), with the fraction of complex cells being almost half in the experimental (35%; orange bar) than in the control group (60%; blue bar).

The lower incidence of complex cells in the experimental group was confirmed when a different metric (the F1/F0 ratio; see Materials and Methods) was applied to quantify the modulation of neuronal responses at the temporal frequency of the gratings (fig. S3; see Discussion for a thorough comparison among the MI and F1/F0 indices and an explanation of why our main analyses have been carried out using the MI). We also verified that the difference in the fraction of complex cells found between the two groups was not driven by a few outlier recording sessions. To this aim, we performed a bootstrap analysis in which (i) we obtained 100 surrogate MI distributions for the populations of control and experimental units by sampling with replacement the available sessions for the two groups and (ii) we computed the fraction of complex cells found in each surrogate distribution. This allowed estimating the spread of the fraction of complex cells measured in each group, as a result of the variable sampling of the recorded sessions. The overlap between the spreads obtained for the two groups was minimal (fig. S4A) and not significant (fig. S4B; P < 0.05), thus showing that the lower incidence of complex cells in the experimental group was robust against the sampling of V1 units performed across different recordings/animals.

Conversely, no difference was observed between the two groups in terms of orientation tuning (Fig. 2C), with the OSI distributions (blue and orange curves) and their medians (dashed lines) being statistically undistinguishable, as well as the fraction of sharply orientation-tuned units (i.e., neurons with an OSI of >0.6; blue versus orange bar). A similar result was found for direction tuning (fig. S5; see Discussion for an interpretation of this finding). Together, these results suggest that our experimental manipulation substantially impaired the development of complex cells but not the emergence of orientation and motion sensitivity.

This conclusion was confirmed by comparing the quality of the RFs inferred through STA for the experimental and control units. To ease the comparison, the pixel intensity values in a STA image were z-scored on the basis of the null distributions of STA values obtained for each pixel, after randomly permuting the association between frames of the movie and spike times, 50 times. This allowed reporting the intensity values of the resulting z-scored STA images in terms of their difference (in units of SD ) from what expected in the case of no frame-related information carried by the spikes. As illustrated by the examples shown in Fig. 3A, we found that STA was as successful at yielding sharp, linear RFs (often similar to Gabor filters) for the experimental units as for the control ones. The sharpness of the STA images, as assessed through an expressly devised contrast index (CI; see Materials and Methods) (34), was similar for the two groups, with the CI distributions and their medians being statistically undistinguishable (Fig. 3B, blue versus orange curve/line). As expected, for both groups, the mean CI was significantly larger for the simple than for the complex cells (dark versus light bars), reflecting the better success of STA at inferring the linear RFs of the former, but no difference was found between the mean CIs of the simple cells of the two groups (dark blue versus brown bar) and the mean CIs of the complex cells (light blue versus yellow bar).

(A) Examples of linear RFs inferred through STA for the control (blue frame) and experimental group (orange frame). In every STA image, each pixel intensity value was independently z-scored on the basis of the null distribution of STA values obtained through a permutation test (see Materials and Methods). (B) Left: Distributions of the CI used to measure the sharpness of the STA images, as obtained for the control (blue; n = 105) and experimental (orange; n = 158) V1 populations. No significant difference was found between the two distributions and their medians (P > 0.05, Kolmogorov-Smirnov test; P > 0.05, Wilcoxon test). Right: Mean values ( SEM) of the CIs computed separately for the simple (dark bars; n = 20, control; n = 49, experimental) and complex (light bars; n = 30, control; n = 26, experimental) cells of the two groups (only units with an OSI of >0.4 included). Within each group, the mean CI was significantly larger for the simple than for the complex cells (**P < 0.01, two-tailed unpaired t test). (C) The color maps showing the distributions of lobe counts (abscissa) for the units with well-defined linear RFs [i.e., within the top quartiles of the CI distributions shown in (B)] in the control (blue; n = 27) and experimental (orange; n = 37) populations are plotted as a function of the binarization threshold (ordinate) used by the lobe-counting algorithm (see Materials and Methods). For every choice of the threshold, the control and experimental distributions were not significantly different (P > 0.05, Fishers exact test). (D) Same analysis as in (C) but applied to the distributions of RF sizes obtained for the control (blue; n = 27) and experimental (orange; n = 37) populations, as a function of the binarization threshold. Again, no significant difference was found between the two distributions at any threshold level (P > 0.05; Fishers exact test).

To further explore the extent to which the spatial structure of the STA-based RFs was similar for the experimental and control units, we measured the size of the RFs and counted how many distinct lobes they contained (this analysis was applied only to the units with well-defined linear RFs, i.e., to STA images within the top quartiles of the CI distributions shown in Fig. 3B, left). To count the lobes, we binarized each STA image by applying a threshold to the modulus of its intensity values. This allowed identifying the lobes as distinct connected regions that crossed the binarization threshold [a more detailed description of this procedure is provided in Materials and Methods, and a graphical illustration can be found in figure 5B of our previous study (34)]. Since these regions became progressively smaller and fewer as a function of the magnitude of the binarization threshold, we compared the distributions of lobe counts obtained for the experimental and control units across different thresholdsfrom 3.5 to 6.5 . At every tested threshold, the distributions of lobe counts for the two populations were statistically indistinguishable (P > 0.05, Fishers exact test; compare matching rows in Fig. 3C). The same was true for the distributions of RF sizes (compare matching rows in Fig. 3D), with the RF size of a unit being defined as the mean of the lengths of the major and minor axes of the ellipse that best fitted the area covered by the detected lobes. These results confirmed that our experimental manipulation did not alter the spatial tuning properties of V1 units.

Next, we tested the extent to which the experimental units that had been classified as complex cells fully retained the functional properties of this class of neurons. As already shown in the previous section, the key property of complex cells is their ability to fire more persistently than simple cells in response to a continuous, spatiotemporally correlated visual input. This can be understood on the basis of intuitive considerations, i.e., the local invariance of complex cells to (e.g.) translations of their preferred oriented edges. In the original work of Hubel and Wiesel (17), this property emerged when static oriented bars matching the preferred orientation of a complex cell were shown in different RF positions and, despite these translations, were found to elicit strong responses in the recorded unit. More recent investigations of V1 have relied instead on moving stimuli, such as the full-field drifting gratings used in our study, which allow probing at once the invariance properties of all the units recorded with a multielectrode array. In these experiments, the translation invariance of complex cells manifests itself as the phase invariance of the responsedespite the phasic alternation of light and dark oriented stripes, produced by the drifting of the preferred grating across its RF, a complex cell is able to respond to the stimulus with a more sustained, temporally persistent firing, as compared to a simple cell [compare the blue and orange rasters/peristimulus time histograms (PSTHs) in Fig. 2A]. More in general, these persistent, slowly changing responses should be expected every time a complex cell is probed with a spatiotemporally correlated stimulus, such as the noise movies used in our study to map the RFs through STA. From a theoretical point of view, this is consistent with the predictions of UTL models, such as SFA (8, 9), that are based exactly on maximizing the slowness (or persistence) of neuronal responses to learn invariance. Critically, the different persistency of the responses of complex and simple cells to spatiotemporally correlated stimuli is not expected to result from intrinsic differences in terms of membrane excitability, temporal integration of the synaptic inputs or firing dynamics. That is, complex cells are not expected to fire more persistently than simple cells when probed with brief, static stimuli (e.g., a complex cell will not continue to fire persistently in the absence of the stimulus). It is the invariance of the stimulus representation afforded by complex cells that is at the origin of their slower responses. Hence, the more persistent firing of complex cells can only emerge when V1 neurons are tested with spatiotemporally continuous stimuli.

To measure the persistence of neuronal responses in our recorded populations, we computed the time constants of the exponential fits to the autocorrelograms of the spike trains evoked by the noise movies. This analysis was restricted to those units whose firing was strongly modulated at the frequency of variation of the contrast in the noise movies (i.e., 0.1 Hz; see examples in Fig. 4A, top, and see Materials and Methods for details). This ensured that our analysis measured the stimulus-dependent amount of slowness in the neuronal responses, as determined by the interplay between the temporal continuity of the visual stimulus and the transformation invariance afforded by the recorded neurons. As expected, the average time constant was larger for the control than for the experimental units (Fig. 4B). This difference, however, was not merely driven by the larger fraction of complex cells in the control group (Fig. 2B). While the average time constants did not significantly differ between the simple cells of the two groups (Fig. 4C, dark blue versus brown bar), the responses of complex cells unfolded over a shorter time scale for the experimental than for the control units (yellow versus light blue bar).

(A) Top: PSTHs showing the average responses of the two example neurons of Fig. 2A to the contrast-modulated noise movies (see Materials and Methods). For both neurons, the firing rate was strongly modulated at the frequency of variation of the contrast of the movies (i.e., 0.1 Hz). Bottom: Distributions of interspike intervals (ISIs) of the spike trains evoked by the noise movies for the two example neurons. The resulting autocorrelograms were fitted with exponential decaying functions (dashed lines) to measure the slowness (i.e., the time constant of the exponential fit) of the responses. In this example, the complex cell (blue curve) displays slower dynamics (i.e., larger ) than the simple cell (orange curve). The two units also differ in the number of counts at low ISIs, which is much larger for the simple cell, as expected for a unit firing tightly packed trains of spikes (see Fig. 2A). (B) Mean values ( SEM) of the time constants computed for the control (blue; n = 92) and experimental (orange; n = 143) populations (***P < 0.001, two-tailed unpaired t test). (C) Mean values ( SEM) of the time constants computed separately for the simple (dark bars; n = 43, control; n = 89, experimental) and complex (light bars; n = 49, control; n = 54, experimental) cells of the two groups. While the simple cells had equally fast dynamics, the complex cells were significantly slower in the control than in the experimental group (**P < 0.01, two-tailed unpaired t test).

To understand the functional implication of these abnormally fast-changing stimulus representations, we assessed the ability of the four distinct populations of simple and complex cells of the two groups to support stable decoding of stimulus orientation over time. To this aim, we randomly sampled 300 neurons from each population (after having first matched the populations in terms of OSI and orientation preference distributions; see Materials and Methods) so as to obtain four equally sized and similarly tuned pseudo-populations whose units homogenously covered the orientation axis. We then trained binary logistic classifiers to discriminate between 0- and 90-oriented gratings (drifting at 4 Hz) based on the activity of each pseudo-population. Each classifier was trained using neuronal responses (spike counts) in a 33-ms-wide time bin that was randomly chosen within the presentation epoch of the gratings. We then tested the ability of each classifier to generalize the discrimination to test bins at increasingly larger time lags (TLs) from the training bin (see Fig. 5A and Materials and Methods for details). As expected, given the strong phase dependence of their responses (see cartoon in Fig. 5A, top), the simple cells from both groups yielded generalization curves that were strongly modulated over time and virtually identical (Fig. 5B, dark blue and brown curves). The performance was high (80% correct) at test bins where the phase of the grating was close to that of the training bin (i.e., at TLs that were multiple of the 250-ms grating period), but it dropped to less than 30% correct (i.e., well below chance; dashed line) at test bins where the grating was in opposition of phase with respect to the training bin (e.g., at a TL of ~125 ms). By comparison, the complex cells of the control group, by virtue of their weaker phase dependence (see cartoon in Fig. 5A, bottom), afforded a decoding of grating orientation that was substantially more phase tolerant, with the performance curve never dropping below chance level at any TL (Fig. 5B, light blue curve). However, for the complex cells of the experimental group, the performance curve (in yellow) was not as stableat most TLs, it was 5 to 10 percentage points smaller than the performance yielded by the control complex (CC) cells, dropping significantly below chance at test bins where the grating was in opposition of phase with respect to the training bin. That is, the ability of the experimental complex (EC) cells to support phase-tolerant orientation decoding was somewhat in between that of properly developed complex cells and that of simple cells. This shows that, even if some complex cells survived our experimental manipulation (i.e., the rearing in temporally broken visual environments), their functional properties were nevertheless impaired by the controlled rearing, as demonstrated by their reduced ability to support phase-invariant decoding of stimulus orientation.

(A) The cartoon illustrates the expected outcome of the decoding analysis to test the ability of simple and complex cells to support phase-tolerant discrimination of grating orientation. In the case of simple cells (top), a linear classifier built at time t0 (middle; light gray shading) to successfully discriminate a vertical from a horizontal drifting grating (left; the filled and empty dots are well separated, within the neuronal representational space, by the linear decision boundary) will generalize poorly when tested at a later time t1 (middle; dark gray shading), with the accuracy dropping even below chance (right; the filled and empty dots swap sides of the linear decision boundary) due to the strong phase dependency of the responses ri (middle; some neurons firing at t0 will stop firing at t1, while some other units that are silent at t0 will respond at t1). By contrast, for a population of complex cells (bottom), given the greater stability of the responses ri (middle), the decision boundary resulting from the training at t0 (left) will generalize better at t1 (right; the filled and empty dots are still mostly on the original side of the decision boundary). (B) Decoding accuracy yielded by the four populations of control simple (dark blue), control complex (light blue), experimental simple (brown), and experimental complex (orange) cells in the vertical (i.e., 90) versus horizontal (i.e., 0) grating discrimination task in 33-ms-wide test bins located at increasingly larger time lags from the training bin (i.e., bin with a lag of 0). The solid curves are the averages of many resampling loops of the neuronal population vectors and the training bins (see Materials and Methods). The shaded regions are the bootstrap-estimated 95% confidence intervals of the solid lines (see Materials and Methods).

The findings reported in our study show that breaking the temporal continuity of early visual experience severely interferes with the typical development of complex cells in V1, leading to a sizable reduction of their number (Fig. 2B) and an impairment of their functional properties (Figs. 4C and 5B). This implies that experience with the temporal contiguity of natural image sequences over time scales longer than 66.7 ms (i.e., the frame duration used during our controlled rearing) plays a critical role in postnatal development of the earliest form of invariance found along the ventral stream. Such an instructive role of temporal continuity of visual stimuli, so far, has been empirically demonstrated only in adult monkeys, at the very last stage of this pathway, the inferotemporal cortex (37). At the same time, our experiments show that degrading the amount of the temporal continuity experienced during development does not affect the emergence of orientation tuning (Fig. 2C), with simple cells exhibiting unaltered spatial (Fig. 3), temporal (Fig. 4C), and functional (Fig. 5B) properties. Interpreting these findings requires a careful discussion of our procedure to classify simple and complex cells, as well as of the strengths and limits of our protocol for controlled rearing, along with a thorough review of the previous studies in which early visual experience was altered during postnatal development.

The original definition of simple cells provided by Hubel and Wiesel (17) was based on the subjective assessment of distinct, elongated ON and OFF flanking regions in the RF of this class of neurons, which endowed them with the property of being both orientation selective and very sensitive to the position of their preferred oriented edges. By contrast, no clearly defined ON and OFF regions could be found for complex cells, which retained the ability to selectively respond to specific orientations, but in a locally position-invariant waya complex cell would still respond vigorously despite displacements of the preferred oriented edge within its RF. Later studies proposed more objective measures to distinguish simple from complex cells (38, 39) by relying instead on the level of modulation of the neuronal response during the presentation of a drifting grating. This approach has gained increased popularity with the advent of multielectrode arrays. Recording many tens of neurons in parallel does not allow probing each individual unit with cell-specific stimuli [such as the oriented bars originally used by Hubel and Wiesel (17)]full-field stimuli (such as drifting gratins) are necessary to simultaneously test the recorded population (34, 35, 40). However, assessing the level of modulation of neuronal firing to distinguish simple from complex cells raises two important issues. The first is methodological and concerns the definition of the most suitable metric to measure response modulation (36). A second, deeper issue concerns the validity itself of the classification of V1 neurons into distinct functional cell types, with some authors proposing that a continuum of cell properties, rather than a segregation into discrete cell classes, better describes the organization of visual cortex (41).

With regard to the first issue, the traditional metric that has been proposed, and is still often used, to characterize response modulation is the so-called F1/F0 ratio, i.e., the ratio between the amplitude of the Fourier spectrum at the temporal frequency of the drifting grating and the mean spike rate of the neuron (see Materials and Methods for details). This metric, however, has been criticized in a recent study (36), which quantitatively demonstrated the already-known drawbacks of the F1/F0 ratio in terms of consistency and reliability. This ratio, in fact, is very sensitive to the relative magnitude of the evoked and background firing rate of a neuron. Specifically, it tends to yield low values not only in the absence of modulation but also when the amplitude of the modulation is weak, relative to the background rate. In this scenario, the F1/F0 ratio tends to underestimate the level of modulation, thus misclassifying as complex cells units that exhibit clearly modulated activity in their PSTHs. In addition, the F1/F0 ratio is not a standardized metric, and the threshold traditionally used to distinguish complex from simple cells (i.e., F1/F0 = 1) is arbitrary and not based on statistical considerations. This led Wypych et al. (36) to define a new modulation metric (which they named standardized F1 or zF1), in which the spectral intensity at the temporal frequency of the drifting grating (i.e., F1) is referred to the mean spectral intensity and divided by its SD. As shown in (36), this metric is more reliable in capturing the level of modulation of neuronal firing that is apparent from the PSTHs. In addition, being a standardized metric, a criterion to distinguish highly modulated (i.e., simple) from poorly modulated (i.e., complex) cells can be defined on statistical grounds, i.e., by measuring how distant F1 is from the mean spectral intensity in units of SD.

In our study, we also used a standardized F1 metric to quantify the level of modulation of neuronal responses to drifting gratings (simply referred to as the MI; see Materials and Methods). This choice was motivated by the considerations explained in the previous paragraph and by having verified, in an earlier study, the effectiveness and robustness of this index at quantifying the level of response modulation not only in rat V1 and higher-level visual cortical regions but also across the layers of deep, artificial neural networks for image classification, such as HMAX and VGG16 (34). Notably, following our adoption of this metric, the key advantages of the standardized F1 index were recently acknowledged by the Allen Institute, which used it for its large-scale surveys of mouse visual cortex (42, 43).

In our current study, for completeness, we have also assessed the modulation of neuronal firing using two different instances of the F1/F0 ratiothe most commonly applied definition (38, 39) and a modified version that has the advantage of being bounded between 0 and 2 (see Materials and Methods) (44). As expected, both F1/F0 ratios tended to inflate the proportion of units falling below the F1/F0 = 1 threshold that is typically used to classify a cell as complex (fig. S3). Despite this reduced sensitivity to capture variations in the level of modulation of the firing rate, the experimental units still displayed a significantly larger response modulation than the control units (orange versus blue curves; P < 0.05, Wilcoxon test). As a result, a significantly lower proportion of experimental cells was classified as complex (orange versus blue bars; P < 0.05, Fishers exact test), thus confirming the impact of rearing newborn rats in visually discontinuous environments on the development of complex cells.

As mentioned above, the debate about the best choice of the modulation metric relates to the deeper issue of whether it is appropriate in the first place to segregate visual cortical neurons into discrete functional classes. Critically, the decoding analysis presented in our study (see Fig. 5) addresses both questions. From a computational perspective, the key functional property distinguishing simple from complex cells is the larger translation invariance that the latter are supposed to afford in the representation of stimulus orientation (16). Modulation metrics measure this ability only indirectly and with a variable degree of reliability. On the other hand, reading-out stimulus orientation using a linear classifier directly quantifies the amount of translation-invariant information that can be easily (i.e., linearly) extracted from the underlying neuronal representation (16). Hence, our decoding analysis (Fig. 5) validates at once the existence of two functionally distinct subpopulations of visual cortical neurons and the metric (i.e., the MI) we used to distinguish them. The radically different degree of phase invariance in the representation of stimulus orientation afforded by the two populations of units classified as simple and complex in the control group (dark versus light blue curves) demonstrates that (i) these populations are indeed functionally distinct, with respect to their ability to code invariantly stimulus orientation; (ii) the MI provides a measure of response modulation that is highly consistent with the degree of translation invariance of the recorded units; and (iii) the 3 threshold used to distinguish simple from complex cells effectively partitions the range of measured MI values into distinct functional classes.

The development of complex cells in the animals reared with the temporally discontinuous movies (i.e., the experimental group) was strongly impaired, with the experimental animals showing a median MI that was almost twice as large as that of the control rats and a fraction of complex cells that was almost half (Fig. 2B). However, it was not fully abolisheda small amount of complex cells survived the experimental manipulation, although with a diminished capability of supporting translation-invariant decoding of stimulus orientation (Fig. 5B). At first glance, this may seem at odd with the hypothesis that temporal continuity is strictly necessary for the development of transformation tolerance in V1. However, it should be considered that, as explained in Results, the disruption of temporal continuity achieved with our controlled rearing was not complete. Even if the frame-scrambled rearing videos lacked temporal structure at time scales longer than 66.7 ms (Fig. 1, B and C), the experimental rats could still experience some residual amount of temporal continuity in the visual experience because of head and/or eye movements. Specifically, the visual features that the animals may have experienced as continuously transforming (e.g., translating) include (i) structural parts of the physical environment (e.g., the edges of the monitors; see fig. S1), (ii) parts of their own bodies, and (iii) the content of individual movie frames, although over very short temporal spans (66.7 ms). As already explained, this residual temporal continuity was not accidental but intentional. It was dictated by the need of allowing the rats full access to the spatial content of the individual image frames, which prevented using frame rates higher than rat flicker fusion frequency (~30 to 40 Hz) (32). In addition, although experience with the motion of physical features and/or body parts may have been strongly limited by the use of head fixation, we preferred to avoid this procedure. In fact, head fixation would have prevented a natural and active exploration of the visual environment, which, in rodents, has been shown to strongly affect the plasticity and development of visual cortex (33)a phenomenon that is consistent with the tight relationship between the encoding of visual and locomotory/positional signals recently reported in rodent V1 (45). The concern that head fixation could limit the impact of controlled visual rearing on the developing visual cortex was reinforced by the failure of a previous study (performed on head-fixed ferrets) to causally demonstrate that experience with oriented visual patterns is necessary for the development of orientation tuning in V1 (24). On the basis of these considerations, we reasoned that the rearing would have been more effective if the newborn rats were left unrestrained inside the immersive visually environments, even at the cost of allowing some residual temporal continuity in their visual experience. The fact that, despite this residual continuity, the development of complex cells was strongly impaired in the experimental rats testifies to the paramount importance of experiencing a fully continuous visual environment for the development of translation tolerance. At the same time, the residual temporal continuity experienced during rearing can easily explain why the development of complex cells was not fully abolished.

The incomplete disruption or temporal continuity during postnatal rearing can also explain why the development of direction selectivity was unaffected by our experimental manipulation (fig. S5). This finding was somewhat unexpected, given that, in agreement with the temporal extension of the sparse coding principle (46), postnatal rearing under stroboscopic illumination has been found to produce a substantial loss of direction selectivity in V1 (2630). This discrepancy with our result can be understood by considering that strobe light flashes in these earlier studies had a much shorter duration (typically, ~10 s) than the frame duration in our movies. Thus, in strobe rearing studies, the animals were fully deprived of experience with smooth motion signals, while our controlled rearing allowed the content of individual image frames to be experienced as smoothly moving (e.g., translating) over time scales of 66.7 ms. On the other hand, our rearing ensured that the temporal correlation of the visual stream delivered through the displays was close to zero over time scales of >66.7 ms (see Fig. 1, B and C). By contrast, strobe rearing, especially at high rates (8 Hz), allowed for such a high-frequency sampling of the visual environment to resemble a normal patterned input (29), leading to human subjective experience [] of a series of jerky images, reminiscent of the early motion picture (26). This implies that, despite the disruption of smooth motion signals at the microsecond time scale, the animals subjected to strobe rearing likely experienced a strongly correlated visual input at time scales as large as several hundreds of milliseconds or a few seconds (i.e., of the order of what experienced by our control rats; see blue curves in Fig. 1, B and C). This likely explains why several studies based on strobe rearing at 4 to 8 Hz mention the existence of complex cells in the strobe-reared animals without explicitly reporting any loss of these neurons (26, 27, 30), with one study, in particular, reporting no qualitative differences in the sampling of simple and complex cells between the strobe-reared and control subjects (28).

In summary, when our results are considered together with those of earlier strobe rearing studies, an intriguing double dissociation emerges with regard to the instructive role of temporal continuity during cortical development. The temporal learning mechanisms leading to the development of invariance appear to be distinct and independent from those supporting the development of direction tuning, with the former operating over time scales that are several orders of magnitude longer than the latter. As a result, successful disruption of temporal continuity at the microsecond time scale but preservation of temporal correlations at time scales of the order of tens/hundreds of milliseconds (as in most strobe rearing studies) interferes with the development of direction tuning but spares the development of complex cells. Vice versa, preserving time contiguity at the microsecond/millisecond level but destroying correlations at longer time scales (as in our study) impairs the development of complex cells without preventing the emergence of direction selectivity.

Another finding of our study that is worth discussing in the context of the limitations of our rearing procedure and previous strobe rearing studies is the typical development of orientation tuning (Fig. 2C) and spatial RF properties (Fig. 3) observed in the experimental rats. Given that the access to the image content of the individual movie frames was the same as for the control animals, this result strongly suggests that development of shape tuning depends on the exposure to the spatial statistics of natural images, rather than on the temporal continuity of the visual stream. Thus, our results would add to the indirect evidence in favor of the role played by USL during development (23, 6). However, given the residual amount of temporal continuity allowed by our rearing procedure, we cannot exclude that, as for the case of direction tuning, development of orientation tuning too may rely on UTL mechanisms working at smaller temporal scales than those required to support the development of invariance. The fact that strobe rearing at 4 to 8 Hz impairs the development of direction tuning but not of orientation selectivity makes this scenario unlikely (2628, 30). Nevertheless, this does not fully exclude the possibility that an intermediate time scale of temporal continuity exists that is necessary for the development of spatial selectivity but is neither sufficiently long to support the development of invariance nor sufficiently short to sustain the development of direction tuning. To settle this question, future studies will need to rear newborn animals with purely static images, possibly varying image duration from a few tens of milliseconds to a few tens of microseconds in different experimental groups. This will require combing head fixation with eye tracking in closed-loop experiments, where initiation of a saccade should abort stimulus presentation so as to fully deprive the subjects of the experience of continuous transformations of the visual input at any time scale.

While our findings, as those of previous strobe rearing studies, point to a pivotal, instructive role of early visual experience in determining the tuning properties of visual cortical neurons, the residual amount of complex cells in our experimental animals, as well as the unimpaired tuning for orientation and direction, could also be explained as the result of genetically encoded, experience-independent developmental programs. Support for this hardwiring hypothesis comes from studies in which orientation and direction selectivity in various species was found to be already highly developed at the onset of visual experience, i.e., right after EO (19). However, this does not seem to apply to rat V1 whose functional properties have been reported to remain immature after postnatal rearing in complete darkness (31). This may point to differences not only among species but also among experimental manipulations, since, in many studies, the animals were kept in a normal dark-light cycle before EO. Differently from dark rearing (DR), this procedure allows for a very blurred and dimmed stimulation of the retina through the closed eyelids, which could drive the development of cortical tuning in an experience-dependent way, either by directly evoking neuronal responses or by fostering the generation of waves of spontaneous activity (see next paragraph) (47). In addition, even a few hours of visual experience after EO may be enough to drive fast development of cortical tuning properties, as demonstrated in juvenile ferrets (22). To date, the most convincing demonstration of experience- and activity-independent formation of orientation and direction tuning comes from a mouse study in which DR was paired with genetic silencing of spontaneous cortical activity during development (48) (unfortunately, the study did not test whether complex cells developed normally).

The possible role played by spontaneously generated activity in instructing the development of cortical tuning is yet another explanation for the residual fraction of complex cells and the unaltered orientation and direction selectivity found in our study. Key to this concept, often referred to as innate learning (49), is the idea that, during development, neural circuits, by virtue of their genetically determined structure, could self-generate activity patterns that are able to act as training examples to sculpt and refine their own wiring or the wiring of other downstream circuits. This activity-dependent structuring may be driven by the same unsupervised plasticity rules (such as USL and UTL) that would later act on stimulus-evoked activity after the onset of sensory experience. An example of innate learning is the role played by the spatiotemporally correlated patterns of activity evoked by retinal waves in driving the development of topographic visual maps (50). From a theoretical standpoint, computational studies have shown that these spontaneous activity patterns could also support the development of simple and complex cells via, respectively, sparse coding (49, 51) and temporal learning mechanisms (52). This may explain the finding of a recent study, where the presence of complex cells in mouse V1 was reported at EO already (40). However, the animals included in that study were not subjected to DR and were also allowed normal visual experience for several hours before the neuronal recordings. This makes it difficult to infer what developmental mechanism was at the origin of the complex cells reported by (40)whether experience-dependent or independent and, in the latter case, whether activity-driven (innate learning) or purely genetically encoded.

In summary, it is difficult to fully reconcile the conclusions of the studies reviewed in the previous two sections, especially given the variability found across species and the variety of experimental approaches that have been devised to manipulate visual experience and/or retinal/cortical activity during early postnatal development. This makes it hard to know whether our altered rearing acted on visual cortical circuits in a blank, immature state or rather reshaped the wiring of circuits that had already been structured by innate developmental programs, possibly combined with the effect of internally generated activity. Nevertheless, what our data causally demonstrate is that a form of plasticity based on UTL must be at work in the developing visual cortex to build up (or maintain) invariance in a way that is highly susceptible to the degree of temporal correlation of visual experience.

From a theoretical standpoint, this result causally validates the family of UTL models (715) at the neural level, albeit strongly suggesting that their scope is limited to the development of invariance and not of shape selectivity. More in general, since slowness has been related to predictability (5355), our results are also consistent with normative approaches to sensory processing that are based on temporal prediction (56). On the other hand, our findings, by showing that exposure to the spatial structure of natural images alone is not enough to enable proper development of complex cells, reject computational accounts of invariance based exclusively on USL (3, 4) while leaving open the possibility that the latter may govern the development of shape tuning (1, 2, 5, 6). As a result, our study tightly constrains unsupervised models of visual cortical development, supporting theoretical frameworks where the objectives of sparseness and slowness maximization coexist to yield, respectively, shape selectivity and transformation tolerance (13, 14, 57).

All animal procedures were in agreement with international and institutional standards for the care and use of animals in research and were approved by the Institutional Animal Care and Use Committee of the International School for Advanced Studies (SISSA) and by the Italian Ministry of Health (project DGSAF 22791-A, submitted on 7 September 2015 and approved on 10 December 2015, approval 1254/2015-PR).

Data were obtained from 18 Long-Evans male rats that were born and reared in our facility for visually controlled rearing. The facility consists of a small vestibule, where the investigators can wear the infrared goggles that are necessary to operate in total darkness, and a larger, lightproof room containing a lightproof housing cabinet (Tecniplast) and four custom cabinets (Tecniplast) for exposure of the rats to controlled visual environments.

Pregnant mothers (Charles River Laboratories) where brought into the housing cabinet about 1 week before delivery. Pups were born inside the cabinet and spent the first 2 weeks of their life in total darkness with their mothers. Starting from P14 (i.e., at EO) until P60 (i.e., well beyond the end of the critical period), each rat, while still housed in full darkness (i.e., inside the housing cabinet) with his siblings, was also subjected to daily 4-hour-long exposures inside an immersive visual environment (referred to as the virtual cage), consisting of a transparent basin (480 mm by 365 mm by 210 mm; Tecniplast 1500 U), fully surrounded by four computer-controlled LCD monitors (one per wall; 20 HP P202va; see fig. S1), and placed on the shelf of one of the custom cabinets (each cabinet had four shelves, for a total of 16 rats that could be simultaneously placed in the visually controlled environments). These controlled rearing environments, which are reminiscent of those used to study the development of object vision in chicks (25), were custom-designed in collaboration with Videosystem, which took care of building and installing them inside the custom cabinets.

Different visual stimuli were played on the monitors, depending on whether an animal was assigned to the experimental or the control group. Rats in the control group (n = 8) were exposed to natural movies, including both indoor and outdoor scenes, camera self-motion, and moving objects. Overall, the rearing playlist included 16 videos of different duration, lasting from a few minutes to half an hour. The playlist was played in random order and looped for the whole duration of the exposure. Rats from the experimental group (n = 10) were exposed to a time-shuffled version of the same movies, where the order of the frames within each video was randomly permuted so as to destroy the temporal continuity of the movie (see Fig. 1, B and C) while leaving unaltered the natural spatial statistics of the individual image frames. All movies were played at 15 Hz, which is approximately half of the critical flicker fusion frequency (~30 to 40 Hz) that has been measured for the rat (32), to make sure that the animals could experience the image content of the individual frames of the movies. Animal care, handling, and transfer operations were always executed in absolute darkness using night vision goggles (Armasight NXY7) in such a way to prevent any unwanted exposure of the animals to visual inputs different from those chosen for the rearing.

To assess the level of temporal structure in the videos that were administered to the control and experimental rats during the controlled rearing inside the virtual cages, we computed the average pixel-level temporal autocorrelation function for each movie. This function was then fitted with an exponential decay model whose time constant provided a measure of the time scale of temporal continuity in the movie.

The first step to compute the temporal autocorrelation function was to chunk each frame in a movie into blocks of 6 6 pixels and then average the pixel intensity values inside each block so as to lower the resolution of the movie frames. This downsampling was necessary to ease the computational load of the analysis. Each movie frame was then unrolled into a vector, and the correlation matrix of the ordered ensemble of frame vectors was computed. Last, all the elements of the correlation matrix that were located along the kth diagonal (where k denotes the distance from the main diagonal) were averaged to obtain the value of the mean temporal autocorrelation function at lag k (with k ranging from 1 to the maximal separation between two frames in a movie).

The following exponential model was used to fit the mean temporal autocorrelation function obtained for each movief(t)=Aet+Cwhere t is the TL (obtained by multiplying the frame lag k by the frame duration of 66.7 ms) and is the time constant of the exponential decay whose value was taken as a measure of the amount of temporal structure in a movie. A and C are free parameters. Only the first 4.95 s of the mean temporal autocorrelation functions were taken into account for the fitting procedure (see Fig. 1, B and C).

Acute extracellular recordings were performed between P60 and P90 (last recording). During this 30-day period, the animals waiting to undergo the recording procedure were maintained on a reduced visual exposure regime (i.e., 2-hour-long visual exposure sessions every second day; see previous section).

The surgery and recording procedure was the same as described in (34). Briefly, the day of the experiment, the rat was taken from the rearing facility and immediately (within 5 to 10 min) anesthetized with an intraperitoneal injection of a solution of fentanyl (0.3 mg/kg; Fentanest, Pfizer) and medetomidin (0.3 mg/kg; Domitor, Orion Pharma). A constant level of anesthesia was then maintained through continuous intraperitoneal infusion of the same aesthetic solution used for induction, but at a lower concentration [fentanyl (0.1 mg/kg per hour) and medetomidine (0.1 g/kg per hour)], by means of a syringe pump (NE-1000, New Era Pump Systems). After induction, the rat was secured to a stereotaxic apparatus (SR-5R, NARISHIGE) in flat-skull orientation (i.e., with the surface of the skull parallel to the base of the stereotax), and following a scalp incision, a craniotomy was performed over the target area in the left hemisphere (typically, a 2 mm by 2 mm window), and the dura was removed to allow the insertion of the electrode array. The coordinates of penetration used to target V1 were 6.5 mm posterior from bregma and 4.5 mm left to the sagittal suture (i.e., anteroposterior, 6.5; mediolateral, 4.5). Once the surgical procedure was completed, and before probe insertion, the stereotax was placed on a rotating platform, and the rats left eye was covered with black, opaque tape, while the right eye (placed at 30-cm distance from the monitor) was immobilized using a metal eye-ring anchored to the stereotax. The platform was then rotated in such a way to bring the binocular visual field of the right eye to cover the left side of the display.

Extracellular recordings were performed using either single- (or double-) shank 32- (or 64-) channel silicon probes (NeuroNexus Technologies) with a site recording area of 775 m2 and an intersite spacing of 25 m. After grounding (by wiring the probe to the animals head skin), the electrode was manually lowered into the cortical tissue using an oil hydraulic micromanipulator (typical insertion speed, 5 m/s; MO-10, NARISHIGE), up to the chosen insertion depth (800 o 1000 m from the cortical surface), either perpendicularly or with a variable tilt, between 10 and 30, relative to the vertical to the surface of the skull. Extracellular signals were acquired using a System 3 Workstation (Tucker Davis Technologies) with a sampling rate of 25 kHz.

Since, in rodents, the largest fraction of complex cells is found in layer 5 of V1 (35), our recordings aimed at sampling more densely that layer. This was verified a posteriori (fig. S2) by estimating the cortical depth and laminar location of the recorded units, based on the patterns of visually evoked potentials (VEPs) recorded across the silicon probes used in our recording sessions. More specifically, we used a template-matching algorithm for laminar identification of cortical recording sites that we recently developed and validated in an appositely dedicated methodological study (58). Briefly, the method finds the optimal match between the pattern of VEPs recorded in a given experiment across a silicon probe and a template VEP profile, spanning the whole cortical thickness, that had been computed by merging an independent pool of 18 recording sessions in which the ground-true depth and laminar location of the recording sites had been recovered through histology. The method achieves a cross-validated accuracy of 79 m in recovering the cortical depth of the recording sites and a 72% accuracy in returning their laminar position, with the latter increasing to 83% for a coarser grouping of the layers into supagranular (L1 to L3), granular (L4), and infragranular (L5 and L6).

During a recording session, each animal was presented with (i) 20 repetitions (trials) of 1.5-s-long drifting gratings, made of all possible combinations of two spatial frequencies (0.02 and 0.04 cycles/degree), two temporal frequencies (2 and 4 Hz), and 12 directions (from 0 to 330, in 30 increments); and (ii) 20 different 60-s-long spatially and temporally correlated, contrast modulated, noise movies (34, 35). All stimuli were randomly interleaved, with a 1-s-long interstimulus interval, during which the display was set to a uniform, middle-gray luminance level. To generate the movies, random white noise movies were spatially correlated by convolving them with a Gaussian kernel having full width at half maximum corresponding to a spatial frequency of 0.04 cycles/degree. Temporal correlation was achieved by convolving the movies with a causal exponential kernel with a 33-ms decay time constant. To prevent adaptation, each movie was also contrast modulated using a rectified sine wave with a 10-s period from full contrast to full contrast (35).

Stimuli were generated and controlled in MATLAB (MathWorks) using the Psychophysics Toolbox package and displayed with gamma correction on a 47-inch LCD monitor (SHARP PNE471R) with 1920 1080pixel resolution, a maximum brightness of 220 cd/m2, and spanning a visual angle of 110 azimuth and 60 elevation. Grating stimuli were presented at 60-Hz refresh rate, whereas noise movies were played at 30 Hz.

Single units were isolated offline using the spike sorting package KlustaKwik-Phy (59). Automated spike detection, feature extraction, and expectation maximization clustering were followed by manual refinement of the sorting using a customized version of the Phy interface. Specifically, we took into consideration many features of the candidate clusters: (i) the distance between their centroids and their compactness in the space of the principal components of the waveforms (a key measure of goodness of spike isolation); (ii) the shape of the auto- and cross-correlograms (important to decide whether to merge two clusters or not); (iii) the variation, over time, of the principal component coefficients of the waveform (important to detect and take into account possible electrode drifts); and (iv) the shape of the average waveform (to exclude, as artifacts, clearly nonphysiological signals). Clusters suspected to contain a mixture of one or more single units were separated using the reclustering feature of the graphical user interface (GUI). After the manual refinement step, we included in our analyses only units that were (i) well-isolated, i.e., with less than 0.5% of rogue spikes within 2 ms in their autocorrelogram and (ii) grating-responsive, i.e., with the response to the most effective grating condition being larger than 2 spikes/s (baseline-subtracted) and being larger than six z-scored points relative to baseline activity. The average baseline (spontaneous) firing rate of each well-isolated unit was computed by averaging its spiking activity over every interstimulus interval. These criteria led to the selection of 105 units for the control group and 158 units for experimental group.

The response of a neuron to a given drifting grating was computed by counting the number of spikes during the whole duration of the stimulus, averaging across trials and then subtracting the spontaneous firing rate (see previous section). To quantify the tuning of a neuron for the orientation and direction of drifting gratings, we computed two standard metrics, the OSI and DSI, which are defined as OSI = (Rpref Rortho)/(Rpref) and DSI = (Rpref Ropposite)/(Rpref), where Rpref is the response of the neuron to the preferred direction, Rortho is the response to the orthogonal direction, relative to the preferred one (i.e., Rortho = Rpref + /2), and Ropposite is the response to the opposite direction, relative to the preferred one (i.e., Ropposite = Rpref + ). Values close to one indicate very sharp tuning, whereas values close to zero are typical of untuned units.

Since phase shifts of a grating are equivalent to positional shifts of the whole, two-dimensional sinusoidal pattern, a classical way to assess position tolerance of V1 neurons (thus discriminating between simple and complex cells) is to probe the phase sensitivity of their responses to optimally oriented gratings. Quantitatively, the phase-dependent modulation of the spiking response at the temporal frequency f1 of a drifting grating was quantified by the MI adapted from (36) and used in (34), defined asMI=PS(f1)PSfPS2fPSf2where PS indicates the power spectral density of the stimulus-evoked response, i.e., of the PSTH, and f denotes the average over frequencies. This metric measures the difference between the power of the response at the stimulus frequency and the average value of the power spectrum in units of its SD. The power spectrum was computed by applying the Blackman-Tukey estimation method to the baseline-subtracted, 10-ms binned PSTH. Since the MI is a standardized measure, values greater than 3 can be interpreted as signaling a strong modulation of the firing rate at the stimulus frequency (typical of simple cells), whereas values smaller than 3 indicate poor modulation (typical of complex cells). On this ground, we adopted MI = 3 as a threshold for classifying neurons as simple or complex. The choice of this classification criterion and the use of the MI itself were determined before seeing the data collected for the current study, exclusively on the basis of our experience with the same metric and criterion in a previous study (34).

We also quantified the phase sensitivity of the recoded neurons using two other popular metrics of response modulation: the standard F1/F0 ratio and a modified version of this metric that has the advantage of being bounded between 0 and 2 (we will refer to this metric as F1/F0*). The F1/F0 ratio (38, 39) is typically defined asF1/F0=F1F0where F1 is the value of the amplitude of the Fourier spectrum at the stimulus frequency f1, whereas F0 is its value at the zero frequency f0 (i.e., the DC or constant component of the response), that isF1=AS(f1)F0=AS(f0=0)On the other hand, the F1/F0* ratio (44) has been defined asF1/F0*=2F1(F0+F1)This allows obtaining an index that is bounded to have a maximum value of 2 rather than infinity (as in the case of the F1/F0 ratio). The amplitude spectra used to compute the F1/F0 and F1/F0* ratios were obtained by subjecting each trial of the preferred grating orientation of a neuron to Fourier analysis. Trials with a firing rate of <2 Hz were excluded from the analysis. Specifically, Fourier amplitude spectra were obtained by applying the fast Fourier transform algorithm to the baseline-subtracted, 10-ms binned PSTH of the steady-state grating response (i.e., from 250 to 1500 ms after stimulus onset). As done in previous studies (39, 44), the threshold we adopted to classify neurons as simple or complex via these ratios was 1 for both indices.

We used the STA method (60) to estimate the linear RF structure of each recorded neuron. The method was applied to the spike trains fired by neurons in response to the spatiotemporally correlated and contrast modulated noise movies described above. To account for the correlation structure of our stimulus ensemble and prevent artifactual blurring of the reconstructed filters, we decorrelated the raw STA images by dividing them by the covariance matrix of the whole stimulus ensemble (60). We used Tikhonov regularization to handle covariance matrix inversion. Statistical significance of the STA images was then assessed pixel-wise by applying the following permutation test. After randomly reshuffling the spike times, the STA analysis was repeated multiple times (n = 50) to derive a null distribution of intensity values for the case of no linear stimulus-spike relationship. This allowed z-scoring the actual STA intensity values using the mean and SD of this null distribution. The temporal span of the spatiotemporal linear kernel we reconstructed via STA extended until 330 ms before spike generation (corresponding to 10 frames of noise at 30-Hz frame rate). The STA analysis was performed on downsampled noise frames (16 32 pixels), and the resulting filters were later spline-interpolated at higher resolution for better visualization.

To estimate the amount of signal contained in a given STA image, we used the CI metric that we have introduced in a previous study (34) (see the method section and figure 5A of that study). The CI is a robust measure of maximal local contrast in a z-scored STA image. Since the intensity values of the original STA images were expressed as z scores (see above), a given CI value can be interpreted in terms of peak-to-peak (i.e., white-to-black) distance in sigma units of the z-scored STA values. For the analysis shown in Fig. 3B, the STA image with the highest CI value was selected for each neuron.

We also characterized the structural complexity of the RFs yielded by STA by counting the number of excitatory/inhibitory lobes that were present in a STA image and measuring the overall size of the resulting RF. The procedure is the same described in our previous study (34) (see the method section and figure 5B of that study). Briefly, we applied a binarization threshold over the modulus of the z-score values of the image (ranging from three to six units of SDs). We then computed the centroid positions of the simply connected regions within the resulting binarized image (i.e., the candidate lobes) and their center of mass (i.e., the candidate RF center). Last, we applied a refinement procedure, which is detailed in (34), to prune spurious candidate lobes (often very small) that were far away from the RF center. Obviously, the number of lobes and the size of the RF (computed as the mean of the major and minor axes of the ellipse that best fitted the region covered by the detected lobes) depended on the binarization threshold. For this reason, in Fig. 3 (C and D), we have compared the lobe number and the RF size of the recorded populations of experimental (orange) and control (blue) units over a range of possible choices of this threshold.

For each neuron, we quantified the slowness of its response to the same noise movies used to estimate its RF by computing the time constant of the autocorrelogram of the evoked spike trains [i.e., the probability density function of interspike intervals (ISI)]. Being the noise movies composed of richer visual patterns than drifting gratings (i.e., richer orientation and spatial frequency content), this was a way to assess the response properties of the recorded population in a slightly more naturalistic stimulation regime. The time constants were computed by fitting autocorrelograms with the following exponential functionf(t)=Aet+Cwhere t is the ISI (see Fig. 4A, bottom) and is the time constant of the exponential decay whose value was taken as a measure of the slowness of the response of each neuron to the noise movies. A and C are free parameters. Only the first 200 ms of the ISI distributions were taken into account for the fitting procedure (see Fig. 4A, bottom).

Only neurons that were strongly modulated at the frequency of variation of the contrast in the movies (i.e., 0.1 Hz) were included in the analysis. To select the neurons that met this criterion, the level of response modulation was quantified by a standardized contrast MI (MIc). The MIc was defined exactly as the MI that was used to assess the phase sensitivity of the responses to the gratings (see above), with the only difference that the target frequency to measure PS(f1) (i.e., the power spectral density at the frequency of the modulated input) was now the frequency of the contrast modulation in the noise movies (i.e., 0.1 Hz). To this aim, we built PSTHs for the noise movies by considering each of the 20 different movies we presented as a different trial of the same stimulus so as to highlight the effect of contrast modulation (see examples of highly contrast modulated neurons in Fig. 4A, top). The MIc for each unit was computed over these PSTHs, and only units with a MIc of >3 (i.e., units that were significantly contrast modulated) were included in the analysis. Furthermore, to ensure a robust estimation of the response time constants, we rejected units for which the R2 (coefficient of determination) of the fit with the best exponential model was lower than 0.5.

The goal of this analysis was to build four pseudo-populations of neuronsi.e., control simple (CS), control complex (CC), experimental simple (ES), and experimental complex (EC) cellswith similar distributions of orientation tuning and orientation preference and then compare their ability to support stable decoding of the orientation of the gratings over time. The pseudo-populations were built as follows. We first matched the control and experimental populations in terms of the sharpness of their orientation tuning. To this aim, we took the OSI distributions of the two populations (i.e., the blue and orange curves in Fig. 2C), and for each bin b in which the OSI axis had been divided (i.e., 10 equispaced bins of size = 0.1), we took as a reference the population with the lowest number of units Nb in that bin. For this population, all the Nb units were considered, while for the other population, Nb units were randomly sampled (without replacement) from those with OSI falling in the bin b. Repeating this procedure for all the 10 bins, we obtained two downsampled populations of control and experimental units, having all the same OSI distribution and the same number of units (n = 92). When considering separately the pools of simple and complex cells within these downsampled populations, the resulting mean OSIs were very similar (CS: 0.44 0.04, n = 43; CC: 0.42 0.03, n = 49; ES: 0.46 0.03, n = 57; EC: 0.38 0.04, n = 35) and not statistically different pairwise (P > 0.05, two-tailed unpaired t test). Matching the four populations in terms of the OSI was essential, but not sufficient, to make sure that they had approximately the same power to support discrimination of the oriented gratings. The populations could still differ in terms of the distributions of orientation preference. To also equate them in this sense and make sure that all possible orientations were equally discriminable, we replicated each unit 11 times by circularly shifting its tuning curve of 11 incremental steps of 30. This yielded four final pseudo-populations of 473 (CS), 539 (CC), 627 (ES), and 385 (EC) units, with matched orientation tuning and homogeneous orientation preference to be used for the decoding analysis.

The latter worked as follows. From each pseudo-population, we sampled (without replacement) 300 units (referred to as decoding pool in what follows) and built 300-dimensional population vectors having as components the responses (i.e., spike counts) of the sampled units in randomly selected presentations (i.e., trials) of either the 0- or the 90-oriented grating (drifting at 4 Hz), with each response computed in the same, randomly chosen 33-ms-wide time bin within the presentation epoch of the grating. More specifically, this time bin was chosen under the constraint of being between 561 and 957 ms from the onset of stimulus presentation so that the drifting grating continued for at least two full cycles (i.e., 561 ms) after the selected bin. The random sampling of the trial to be used in a given population vector was performed independently for each neuron (and without replacement) so as to get rid of any noise correlation among the units that were recorded in the same session. Given that 20 repeated trials were recorded per neuron and stimulus condition, a set of 20 population vectors was built for the 0-oriented grating and another set for the 90-oriented gratings. These vectors were used to train a binary logistic classifier to discriminate the two stimuli. The resulting classifier was then tested for its ability to discriminate the gratings in 33-ms-wide test bins that were increasingly distant (in time) from the training bin, covering two full cycles of the drifting gratings (i.e., from 33 to 561 ms following the training bin; see abscissa in Fig. 5B). This analysis was repeated for 50 random samplings (without replacement) of the decoding pools and, given a decoding pool, for 10 independent random draws (without replacement) of the training time bin. The resulting 500 accuracy curves were then averaged to yield the final estimate of the stability of the classification over time (solid curves in Fig. 5B).

To obtain 95% confidence intervals (shaded regions in Fig. 5B) for these average classification curves, we run a bootstrap analysis that worked as follows. For each of the four pseudo-populations, we sampled (with replacement) 50 surrogate populations and used those to rerun the whole decoding analysis described in the previous paragraph. This yielded 50 bootstrap classification curves that were used to compute SEs for the actual generalization curve. The SEs were then converted into confidence intervals by multiplying them by the appropriate critical value of 1.96.

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Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells - Science...

New York, May 29, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Grow Lights Market by Offering, Installation Type, Application, Lighting Type, Watt And Geography - Global Forecast to 2025" - https://www.reportlinker.com/p04801483/?utm_source=GNW Further, the rise of vertical farms, technological advancements in LED grow lights, the legalization of cannabis in different countries including the US, and the emergence of horticulture lighting software and calculator will create opportunities for the grow lights market.

The major restraint for the market is the high setup and installation costs. The lack of standard testing practices for assessing product quality of grow lights and their fixtures pose a major challenge to this market.

New installations to hold larger share of grow lights market during forecast period The demand for fresh horticultural produce is increasing with the growing population.This is expected to encourage growers to set up new greenhouses and expand their existing production facilities to cultivate higher yield each year.

The emergence of vertical farms, particularly in urban settings, is also contributing to the overall increase in horticultural output. Plants grown in vertical farms are entirely dependent on artificial lighting for photosynthesis; this factor is driving the growth of the market for new installations.

Among all applications, commercial greenhouses held largest share of grow lights market in 2019 The commercial greenhouse accounted for the largest share of nearly 47% of the grow lights market, by application, in 2019.Commercial greenhouses have witnessed increased automation in the last decade, and the concept of controlled environment agriculture (CEA) is being implemented in greenhouses to maintain optimum growing conditions and obtain a higher yield.

Growers are gradually realizing the potential benefits of cultivating plants inside a greenhouse; this has contributed to the development of commercial greenhouses.

Europe to hold largest share of grow lights market during forecast period Europe is expected to continue to dominate the grow lights market during the forecast period.This region has been using grow light systems for the past few decades in controlled environment agriculture (CEA) such as greenhouses, vertical farming, and indoor farming.

The use of grow lights in this region is gradually increasing; from being a supplemental lighting source, grow lights are becoming the primary source of light in indoor farming operations.The population of Europe has grown significantly in recent years, and countries in the region are importing fruits and vegetables from the external markets of Africa and Asia in frozen form.

To reduce dependency on imports, some of the major countries in this region are increasingly adopting CEA to obtain fresh produce from locally cultivated farms. This, in turn, is expected to generate a huge demand for grow light systems in the future. Signify Holding (Netherlands), General Electric Company (US), Osram GmbH (Germany), Gavita International B. V. (Netherlands), Helliospectra AB (Sweden), Iwasaki Electric Co., Ltd. (Japan), Illumitex (US), Hortilux Schrder (Netherlands), California Lightworks (US), Zuzi Technology (China), AeroFarms (US), Emium LLC (US), InduLux Technologies (Canada), Valoya (Finland), Kessil (US), Thrive Agritech (US), VividGro (US), Bowery Farming, Inc. (US), Metropolis Farms (US), and Crop One Holdings (US) are a few major players in the grow lights market.

Breakdown of primary participants profile: By Company Type: Tier 1 = 35%, Tier 2 = 45%, and Tier 3 = 20% By Designation: C-level Executives = 40%, Directors = 25%, and Others = 35% By Region: North America = 40%, Europe = 30%, APAC = 20%, and RoW = 10%

Research Coverage: Watt, lighting type, installation type, offering, application, and geography are the segments covered in this report. The report gives a detailed view of the market across 4 main regions: North America, Europe, APAC, and RoW.

Reasons to Buy the Report: This report includes statistics pertaining to the grow lights market in terms of watt, lighting type, installation type, offering, application, geography, along with their respective market sizes. Major drivers, restraints, opportunities, and challenges for the grow lights market have been provided in detail in this report. The report includes illustrative segmentation, analysis, and forecast for the grow lights market based on its segments and subsegments.Read the full report: https://www.reportlinker.com/p04801483/?utm_source=GNW

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Originally posted here:
The grow lights market is projected to reach USD 2.5 billion in 2025 from USD 1.0 billion in 2020, at CAGR of 19.9% - GlobeNewswire

BEFORE: Before the renovations, the home was a new construction, built in 2019. The layout felt closed-off and lacked personality.

AFTER: Shanty opened up the floor plan and added a variety of textures, colors, and patterns. We also created a sense of warmth and coziness to the home with a cohesive color palette and plenty of room for entertaining and relaxing, she says. The entire property is drenched in bold colors and patterns and an abundance of textures, yet the design elements are still subtle and not overpowering.

Given the location in sunny, beautiful California, lighting and outdoor space was key to transforming the home. Shanty worked extensively to highlight the abundance of natural light, adding new skylights, aluminum wood-clad windows, and glass patio doors all around the house for multiple outdoor access points.

Landscaping was also essential in creating a home that maximized the idea of indoor-outdoor living. We also created viewpoints of the lush greenery and landscaping elements from every window and glass door of the home, says Shanty. Tall mature plants, hedging, and trees offer privacy for the homeowners, even with windows and doors open. The front and side yards and the backyard are all connected through custom-made concrete pathways, allowing for a seamless transition from indoors to outdoors.

BEFORE: The kitchen felt cramped and lacked design elements. Our main goal was to add character and charm to the home, so it could blend in seamlessly with the other homes in the neighborhood, says Shanty.

AFTER: The floor plan was opened up to make the kitchen feel more spacious. Custom wallpaper was added to the kitchen, as well as farmhouse aesthetics such as wood paneling and bright windows.

Outside, Shanty added modern furniture and perhaps one of the highlights of the property: a she shed. Perched behind the hammock and metal pergola bench in the backyard, the custom-designed space can be used as a studio, office, yoga studio, or just as a place to escape when it's most needed. We fully customized this shed with flooring, windows, a maple wood wall paneling, dark ceiling, pendant light, and electrics, says Shanty. The pink pattern cement tile on the entry floor completes the look. We specifically used maple wood wall paneling to give the shed a unique factor and a cabin in the middle of nature vibe.

AFTER: We specifically used maple wood wall paneling to give the shed a unique factor and a cabin in the middle of nature vibe, explains Shanty.

Touches of star jasmine plants and a tranquil copper water fountain flank the outdoor space and create a serene vibe.

AFTER: I love how balanced the overall design, look, and vibe is and how it translates so well to our modern farmhouse theme. The home feels complete and the rooms have their own unique personalities, which tie in nicely with the rest of the property, Shanty says of the bedroom.

AFTER: The outdoor-indoor living philosophy is in evidence here, as the doors of the bedroom can be opened to enjoy natural light and the backyard.

AFTER: The guest bathroom contains a marble mosaic floor and Cl Tile thin glazed brick seamless shower.

Allprace Properties also partnered with Humble Design, a nonprofit serving families, veterans, and individuals emerging from homelessness, to have a portion of proceeds from each of its homes sold to sponsor a furnished home for a family transitioning out of homelessness.

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Lighting and Outdoor Space Were Key to Transforming This Modern Farmhouse Renovation - Architectural Digest

NEW YORK, May 27, 2020 /PRNewswire/ --Amid the COVID-19 crisis and the looming economic recession, the Lighting Fixtures and Luminaires market worldwide will grow by a projected US$25.6 Billion, during the analysis period, driven by a revised compounded annual growth rate (CAGR) of 3.8%. Non-Portable, one of the segments analyzed and sized in this study, is forecast to grow at over 4.1% and reach a market size of US$84 Billion by the end of the analysis period. An unusual period in history, the coronavirus pandemic has unleashed a series of unprecedented events affecting every industry. The Non-Portable market will be reset to a new normal which going forwards in a post COVID-19 era will be continuously redefined and redesigned. Staying on top of trends and accurate analysis is paramount now more than ever to manage uncertainty, change and continuously adapt to new and evolving market conditions.

Read the full report: https://www.reportlinker.com/p02438202/?utm_source=PRN

As part of the new emerging geographic scenario, the United States is forecast to readjust to a 2.4% CAGR. Within Europe, the region worst hit by the pandemic, Germany will add over US$630.3 Million to the region's size over the next 7 to 8 years. In addition, over US$643.6 Million worth of projected demand in the region will come from Rest of European markets. In Japan, the Non-Portable segment will reach a market size of US$4.3 Billion by the close of the analysis period. Blamed for the pandemic, significant political and economic challenges confront China. Amid the growing push for decoupling and economic distancing, the changing relationship between China and the rest of the world will influence competition and opportunities in the Lighting Fixtures and Luminaires market. Against this backdrop and the changing geopolitical, business and consumer sentiments, the world's second largest economy will grow at 7.4% over the next couple of years and add approximately US$8.2 Billion in terms of addressable market opportunity. Continuous monitoring for emerging signs of a possible new world order post-COVID-19 crisis is a must for aspiring businesses and their astute leaders seeking to find success in the now changing Lighting Fixtures and Luminaires market landscape. All research viewpoints presented are based on validated engagements from influencers in the market, whose opinions supersede all other research methodologies.

Competitors identified in this market include, among others, Acuity Brands Lighting, Inc.; American Electric Lighting; Amerlux, LLC; Bajaj Electricals Ltd.; Cree, Inc.; Current, Powered by GE; Eaton Corporation plc; ELK Group International, Inc.; Fagerhults Belysning AB; Feilo Sylvania; FW Thorpe Plc; Havells India Limited; Hella KGaA Hueck & Co., ; Holophane, Inc.; Hubbell Lighting, Inc.; Juno Lighting Group; Koito Manufacturing Company Ltd.; LEDvance GmbH; Lithonia Lighting Company; LSI Industries, Inc.; Lutron Electronics Co., Inc.; NVC Lighting Technology Corporation; OMS, a.s.; Opple Lighting; Osram GmbH; Panasonic Corp.; Schrder Group GIE; Targetti Sankey S.p.A.; TCP International Holdings Ltd.; Thomas Lighting; Thorn Licht GmbH; Toshiba Lighting & Technology Corporation; TRILUX GmbH & Co. KG; Venture Lighting International, Inc.; Zumtobel Group AG

Read the full report: https://www.reportlinker.com/p02438202/?utm_source=PRN

LIGHTING FIXTURES AND LUMINAIRES MCP-2MARKET ANALYSIS, TRENDS, AND FORECASTS, JUNE 2CONTENTS

I. INTRODUCTION, METHODOLOGY & REPORT SCOPE

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW Lighting Fixtures and Luminaires: 'Shaping' the Future of Energy Efficient Lighting Technologies Recent Market Activity Bright Prospects Ahead for World Lighting Fixtures & Luminaires Market Developing Asian Countries Drive Current and Future Market Growth Developed Regions Continue to Generate Significant Opportunities Innovations & Advancements: Prime Force Directing the Market Progress Growth for Luminaires Surpasses Lamps in the Global Market for Lighting Equipment Global Competitor Market Shares Lighting Fixtures and Luminaires Competitor Market Share Scenario Worldwide (in %): 2020 & 2029 Impact of Covid-19 and a Looming Global Recession

2. FOCUS ON SELECT PLAYERS Acuity Brands Lighting, Inc. (USA) American Electric Lighting (USA) Holophane, Inc. (USA) Juno Lighting Group (USA) Lithonia Lighting Company (USA) Amerlux, LLC (USA) Bajaj Electricals Ltd. (India) Cree, Inc. (USA) Crompton Greaves Ltd. (India) Current, Powered by GE (USA) Eaton Corporation plc (Ireland) ELK Group International, Inc. (USA) Thomas Lighting (USA) Fagerhults Belysning AB (Sweden) Feilo Sylvania (UK) FW Thorpe Plc (UK) Havells India Limited (India) Hella KGaA Hueck & Co. (Germany) Hubbell Lighting, Inc. (USA) Koito Manufacturing Company Ltd. (Japan) LEDvance GmbH (Germany) LSI Industries, Inc. (USA) Lutron Electronics Co., Inc. (USA) NVC (Huizhou) Lighting Technology Corporation (China) OMS, a.s. (Slovakia) Opple Lighting (China) Osram GmbH (Germany) Panasonic Corporation (Japan) PhotonStar LED Ltd. (UK) Schrder Group GIE (Belgium) Signify N.V. (The Netherlands) Color Kinetics (USA) Strand Lighting (USA) Targetti Sankey S.p.A. (Italy) TCP International Holdings Ltd. (Switzerland) Toshiba Lighting & Technology Corporation (Japan) TRILUX GmbH & Co. KG (Germany) Venture Lighting International, Inc. (USA) Zumtobel Group AG (Austria) Thorn Licht GmbH (Germany)

3. MARKET TRENDS & DRIVERS Surging Demand for LED Lighting Emerges as the Fundamental Growth Driver LED Lamps and Luminaires Penetration Varies Across General Lighting Segments General Lighting Segments: Key Factors Impacting LED Adoption LED Luminaires Uptake in Commercial Buildings to Increase Wider Uptake of LED Lighting in Industrial Facilities Drives Demand for Industrial LED Luminaires Efficiency Requirements Boost LED Luminaires Demand Strong Growth Predicted for LED Luminaires Over the Next Few Years LED Outdoor Luminaires are Poised for Rapid Penetration LED Lamps and Luminaires Adoption Faster in Countries with High Power Costs Future Trends in LED Lighting and Luminaires Market Energy Efficient OLED Luminaires to Witness Increased Adoption Favorable Government Policies Spur Adoption of Energy Efficient Lamps and Luminaires Growing Proliferation of Smart Lighting, Connected Home Technology and Digitization Drive Healthy Market Growth Booming Smart Lighting Market Drives Strong Demand for Intelligent Luminaires Luminaires: The Media of Choice for Enabling the IoT Technology Connected, Embedded, and Sustainable Lighting Drive Enormous Changes in Luminaire Design and Functionality A Brief Review of Select Recently Launched Smart Lighting Solutions Lighting Controls in Fixtures Offer Higher Savings, Energy Efficiency, and Space Efficiency, Augurs Well for Market Expansion Controls for Connected, Intelligent, and Advanced LED Luminaries Robust Growth Opportunities in Industrial Applications Modernization Initiatives Spur Demand for Industrial Luminaires Key Determinants for Lighting Solutions in Industrial Spaces Lighting Technologies for Industrial Spaces: Features, Rated Hours and Applications Industrial & High Baby Lighting Markets: The Most Challenging, Yet Lucrative Applications Stylish and Decorative Luminaires in a Variety of Sizes and Shapes Drive Market Growth for Architectural Fixtures A Brief Review of Latest Architectural Lighting Design Trends Sculptural Lighting Fixtures Stand to Make Gains Construction Activity: A Key Demand Determinant Healthy Momentum in World Construction Sector Offers Bright Prospects Construction Industry Statistical Snapshot Home Decor Trends Drive the Portable Lighting Fixtures Market Automotive Remains A Core End-Use Sector for Lighting Luminaires Stable Automobile Production Bodes Well for Vehicular Lighting Fixtures Positive Demographic and Socio-Economic Trends Strengthen Market Prospects Urbanization Trend Burgeoning Middle Class Population Rising Standards of Living Favorable Economic Scenario Continuous Innovation: Primary Market Characteristic Interior Lighting Design Trends Influence Indoor Luminaires Innovations Review of Select Interior Lighting Design Trends A Review of Select Luminaires Innovations Novel Tracklights that Offer Optimal Flexibility Irrespective of Project Application Other Recent Innovative Fixture and

4. GLOBAL MARKET PERSPECTIVE Table 1: Lighting Fixtures and Luminaires Global Market Estimates and Forecasts in US$ Million by Region/Country: 2020-2027 Table 2: Lighting Fixtures and Luminaires Global Retrospective Market Scenario in US$ Million by Region/Country: 2012-2019 Table 3: Lighting Fixtures and Luminaires Market Share Shift across Key Geographies Worldwide: 2012 VS 2020 VS 2027 Table 4: Non-Portable (Product Segment) World Market by Region/Country in US$ Million: 2020 to 2027 Table 5: Non-Portable (Product Segment) Historic Market Analysis by Region/Country in US$ Million: 2012 to 2019 Table 6: Non-Portable (Product Segment) Market Share Breakdown of Worldwide Sales by Region/Country: 2012 VS 2020 VS 2027 Table 7: Portable (Product Segment) Potential Growth Markets Worldwide in US$ Million: 2020 to 2027 Table 8: Portable (Product Segment) Historic Market Perspective by Region/Country in US$ Million: 2012 to 2019 Table 9: Portable (Product Segment) Market Sales Breakdown by Region/Country in Percentage: 2012 VS 2020 VS 2027 Table 10: Parts & Accessories (Product Segment) Geographic Market Spread Worldwide in US$ Million: 2020 to 2027 Table 11: Parts & Accessories (Product Segment) Region Wise Breakdown of Global Historic Demand in US$ Million: 2012 to 2019 Table 12: Parts & Accessories (Product Segment) Market Share Distribution in Percentage by Region/Country: 2012 VS 2020 VS 2027 Table 13: Residential Lighting (End-Use) Demand Potential Worldwide in US$ Million by Region/Country: 2020-2027 Table 14: Residential Lighting (End-Use) Historic Sales Analysis in US$ Million by Region/Country: 2012-2019 Table 15: Residential Lighting (End-Use) Share Breakdown Review by Region/Country: 2012 VS 2020 VS 2027 Table 16: Automotive Lighting (End-Use) Worldwide Latent Demand Forecasts in US$ Million by Region/Country: 2020-2027 Table 17: Automotive Lighting (End-Use) Global Historic Analysis in US$ Million by Region/Country: 2012-2019 Table 18: Automotive Lighting (End-Use) Distribution of Global Sales by Region/Country: 2012 VS 2020 VS 2027 Table 19: Office Lighting (End-Use) Sales Estimates and Forecasts in US$ Million by Region/Country for the Years 2through 2027 Table 20: Office Lighting (End-Use) Analysis of Historic Sales in US$ Million by Region/Country for the Years 2012 to 2019 Table 21: Office Lighting (End-Use) Global Market Share Distribution by Region/Country for 2012, 2020, and 2027 Table 22: Outdoor Lighting (End-Use) Global Opportunity Assessment in US$ Million by Region/Country: 2020-2027 Table 23: Outdoor Lighting (End-Use) Historic Sales Analysis in US$ Million by Region/Country: 2012-2019 Table 24: Outdoor Lighting (End-Use) Percentage Share Breakdown of Global Sales by Region/Country: 2012 VS 2020 VS 2027 Table 25: Architectural Lighting (End-Use) Worldwide Sales in US$ Million by Region/Country: 2020-2027 Table 26: Architectural Lighting (End-Use) Historic Demand Patterns in US$ Million by Region/Country: 2012-2019 Table 27: Architectural Lighting (End-Use) Market Share Shift across Key Geographies: 2012 VS 2020 VS 2027 Table 28: Other End-Uses (End-Use) Global Market Estimates & Forecasts in US$ Million by Region/Country: 2020-2027 Table 29: Other End-Uses (End-Use) Retrospective Demand Analysis in US$ Million by Region/Country: 2012-2019 Table 30: Other End-Uses (End-Use) Market Share Breakdown by Region/Country: 2012 VS 2020 VS 2027

III. MARKET ANALYSIS GEOGRAPHIC MARKET ANALYSIS UNITED STATES Market Facts & Figures US Lighting Fixtures and Luminaires Market Share (in %) by Company: 2020 & 2025 Market Analytics Table 31: United States Lighting Fixtures and Luminaires Market Estimates and Projections in US$ Million by Product Segment: 2020 to 2027 Table 32: Lighting Fixtures and Luminaires Market in the United States by Product Segment: A Historic Review in US$ Million for 2012-2019 Table 33: United States Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 34: United States Lighting Fixtures and Luminaires Latent Demand Forecasts in US$ Million by End-Use: 2020 to 2027 Table 35: Lighting Fixtures and Luminaires Historic Demand Patterns in the United States by End-Use in US$ Million for 2012-2019 Table 36: Lighting Fixtures and Luminaires Market Share Breakdown in the United States by End-Use: 2012 VS 2020 VS 2027 CANADA Table 37: Canadian Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2to 2027 Table 38: Canadian Lighting Fixtures and Luminaires Historic Market Review by Product Segment in US$ Million: 2012-2019 Table 39: Lighting Fixtures and Luminaires Market in Canada: Percentage Share Breakdown of Sales by Product Segment for 2012, 2020, and 2027 Table 40: Canadian Lighting Fixtures and Luminaires Market Quantitative Demand Analysis in US$ Million by End-Use: 2020 to 2027 Table 41: Lighting Fixtures and Luminaires Market in Canada: Summarization of Historic Demand Patterns in US$ Million by End-Use for 2012-2019 Table 42: Canadian Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 JAPAN Table 43: Japanese Market for Lighting Fixtures and Luminaires: Annual Sales Estimates and Projections in US$ Million by Product Segment for the Period 2020-2027 Table 44: Lighting Fixtures and Luminaires Market in Japan: Historic Sales Analysis in US$ Million by Product Segment for the Period 2012-2019 Table 45: Japanese Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 46: Japanese Demand Estimates and Forecasts for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 47: Japanese Lighting Fixtures and Luminaires Market in US$ Million by End-Use: 2012-2019 Table 48: Lighting Fixtures and Luminaires Market Share Shift in Japan by End-Use: 2012 VS 2020 VS 2027 CHINA Table 49: Chinese Lighting Fixtures and Luminaires Market Growth Prospects in US$ Million by Product Segment for the Period 2020-2027 Table 50: Lighting Fixtures and Luminaires Historic Market Analysis in China in US$ Million by Product Segment: 2012-2019 Table 51: Chinese Lighting Fixtures and Luminaires Market by Product Segment: Percentage Breakdown of Sales for 2012, 2020, and 2027 Table 52: Chinese Demand for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 53: Lighting Fixtures and Luminaires Market Review in China in US$ Million by End-Use: 2012-2019 Table 54: Chinese Lighting Fixtures and Luminaires Market Share Breakdown by End-Use: 2012 VS 2020 VS 2027 EUROPE Market Facts & Figures European Lighting Fixtures and Luminaires Market: Competitor Market Share Scenario (in %) for 2020 & 2025 Market Analytics Table 55: European Lighting Fixtures and Luminaires Market Demand Scenario in US$ Million by Region/Country: 2020-2027 Table 56: Lighting Fixtures and Luminaires Market in Europe: A Historic Market Perspective in US$ Million by Region/Country for the Period 2012-2019 Table 57: European Lighting Fixtures and Luminaires Market Share Shift by Region/Country: 2012 VS 2020 VS 2027 Table 58: European Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2020-2027 Table 59: Lighting Fixtures and Luminaires Market in Europe in US$ Million by Product Segment: A Historic Review for the Period 2012-2019 Table 60: European Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 61: European Lighting Fixtures and Luminaires Addressable Market Opportunity in US$ Million by End-Use: 2020-2027 Table 62: Lighting Fixtures and Luminaires Market in Europe: Summarization of Historic Demand in US$ Million by End-Use for the Period 2012-2019 Table 63: European Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 FRANCE Table 64: Lighting Fixtures and Luminaires Market in France by Product Segment: Estimates and Projections in US$ Million for the Period 2020-2027 Table 65: French Lighting Fixtures and Luminaires Historic Market Scenario in US$ Million by Product Segment: 2012-2019 Table 66: French Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 67: Lighting Fixtures and Luminaires Quantitative Demand Analysis in France in US$ Million by End-Use: 2020-2027 Table 68: French Lighting Fixtures and Luminaires Historic Market Review in US$ Million by End-Use: 2012-2019 Table 69: French Lighting Fixtures and Luminaires Market Share Analysis: A 17-Year Perspective by End-Use for 2012, 2020, and 2027 GERMANY Table 70: Lighting Fixtures and Luminaires Market in Germany: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 71: German Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 72: German Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 73: Lighting Fixtures and Luminaires Market in Germany: Annual Sales Estimates and Forecasts in US$ Million by End-Use for the Period 2020-2027 Table 74: German Lighting Fixtures and Luminaires Market in Retrospect in US$ Million by End-Use: 2012-2019 Table 75: Lighting Fixtures and Luminaires Market Share Distribution in Germany by End-Use: 2012 VS 2020 VS 2027 ITALY Table 76: Italian Lighting Fixtures and Luminaires Market Growth Prospects in US$ Million by Product Segment for the Period 2020-2027 Table 77: Lighting Fixtures and Luminaires Historic Market Analysis in Italy in US$ Million by Product Segment: 2012-2019 Table 78: Italian Lighting Fixtures and Luminaires Market by Product Segment: Percentage Breakdown of Sales for 2012, 2020, and 2027 Table 79: Italian Demand for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 80: Lighting Fixtures and Luminaires Market Review in Italy in US$ Million by End-Use: 2012-2019 Table 81: Italian Lighting Fixtures and Luminaires Market Share Breakdown by End-Use: 2012 VS 2020 VS 2027 UNITED KINGDOM Table 82: United Kingdom Market for Lighting Fixtures and Luminaires: Annual Sales Estimates and Projections in US$ Million by Product Segment for the Period 2020-2027 Table 83: Lighting Fixtures and Luminaires Market in the United Kingdom: Historic Sales Analysis in US$ Million by Product Segment for the Period 2012-2019 Table 84: United Kingdom Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 85: United Kingdom Demand Estimates and Forecasts for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 86: United Kingdom Lighting Fixtures and Luminaires Market in US$ Million by End-Use: 2012-2019 Table 87: Lighting Fixtures and Luminaires Market Share Shift in the United Kingdom by End-Use: 2012 VS 2020 VS 2027 SPAIN Table 88: Spanish Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2to 2027 Table 89: Spanish Lighting Fixtures and Luminaires Historic Market Review by Product Segment in US$ Million: 2012-2019 Table 90: Lighting Fixtures and Luminaires Market in Spain: Percentage Share Breakdown of Sales by Product Segment for 2012, 2020, and 2027 Table 91: Spanish Lighting Fixtures and Luminaires Market Quantitative Demand Analysis in US$ Million by End-Use: 2020 to 2027 Table 92: Lighting Fixtures and Luminaires Market in Spain: Summarization of Historic Demand Patterns in US$ Million by End-Use for 2012-2019 Table 93: Spanish Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 RUSSIA Table 94: Russian Lighting Fixtures and Luminaires Market Estimates and Projections in US$ Million by Product Segment: 2020 to 2027 Table 95: Lighting Fixtures and Luminaires Market in Russia by Product Segment: A Historic Review in US$ Million for 2012-2019 Table 96: Russian Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 97: Russian Lighting Fixtures and Luminaires Latent Demand Forecasts in US$ Million by End-Use: 2020 to 2027 Table 98: Lighting Fixtures and Luminaires Historic Demand Patterns in Russia by End-Use in US$ Million for 2012-2019 Table 99: Lighting Fixtures and Luminaires Market Share Breakdown in Russia by End-Use: 2012 VS 2020 VS 2027 REST OF EUROPE Table 100: Rest of Europe Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2020-2027 Table 101: Lighting Fixtures and Luminaires Market in Rest of Europe in US$ Million by Product Segment: A Historic Review for the Period 2012-2019 Table 102: Rest of Europe Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 103: Rest of Europe Lighting Fixtures and Luminaires Addressable Market Opportunity in US$ Million by End-Use: 2020-2027 Table 104: Lighting Fixtures and Luminaires Market in Rest of Europe: Summarization of Historic Demand in US$ Million by End-Use for the Period 2012-2019 Table 105: Rest of Europe Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 ASIA-PACIFIC Table 106: Asia-Pacific Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Region/Country: 2020-2027 Table 107: Lighting Fixtures and Luminaires Market in Asia-Pacific: Historic Market Analysis in US$ Million by Region/Country for the Period 2012-2019 Table 108: Asia-Pacific Lighting Fixtures and Luminaires Market Share Analysis by Region/Country: 2012 VS 2020 VS 2027 Table 109: Lighting Fixtures and Luminaires Market in Asia-Pacific by Product Segment: Estimates and Projections in US$ Million for the Period 2020-2027 Table 110: Asia-Pacific Lighting Fixtures and Luminaires Historic Market Scenario in US$ Million by Product Segment: 2012-2019 Table 111: Asia-Pacific Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 112: Lighting Fixtures and Luminaires Quantitative Demand Analysis in Asia-Pacific in US$ Million by End-Use: 2020-2027 Table 113: Asia-Pacific Lighting Fixtures and Luminaires Historic Market Review in US$ Million by End-Use: 2012-2019 Table 114: Asia-Pacific Lighting Fixtures and Luminaires Market Share Analysis: A 17-Year Perspective by End-Use for 2012, 2020, and 2027 AUSTRALIA Table 115: Lighting Fixtures and Luminaires Market in Australia: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 116: Australian Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 117: Australian Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 118: Lighting Fixtures and Luminaires Market in Australia: Annual Sales Estimates and Forecasts in US$ Million by End-Use for the Period 2020-2027 Table 119: Australian Lighting Fixtures and Luminaires Market in Retrospect in US$ Million by End-Use: 2012-2019 Table 120: Lighting Fixtures and Luminaires Market Share Distribution in Australia by End-Use: 2012 VS 2020 VS 2027 INDIA Table 121: Indian Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2to 2027 Table 122: Indian Lighting Fixtures and Luminaires Historic Market Review by Product Segment in US$ Million: 2012-2019 Table 123: Lighting Fixtures and Luminaires Market in India: Percentage Share Breakdown of Sales by Product Segment for 2012, 2020, and 2027 Table 124: Indian Lighting Fixtures and Luminaires Market Quantitative Demand Analysis in US$ Million by End-Use: 2020 to 2027 Table 125: Lighting Fixtures and Luminaires Market in India: Summarization of Historic Demand Patterns in US$ Million by End-Use for 2012-2019 Table 126: Indian Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 SOUTH KOREA Table 127: Lighting Fixtures and Luminaires Market in South Korea: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 128: South Korean Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 129: Lighting Fixtures and Luminaires Market Share Distribution in South Korea by Product Segment: 2012 VS 2020 VS 2027 Table 130: Lighting Fixtures and Luminaires Market in South Korea: Recent Past, Current and Future Analysis in US$ Million by End-Use for the Period 2020-2027 Table 131: South Korean Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by End-Use: 2012-2019 Table 132: Lighting Fixtures and Luminaires Market Share Distribution in South Korea by End-Use: 2012 VS 2020 VS 2027 REST OF ASIA-PACIFIC Table 133: Rest of Asia-Pacific Market for Lighting Fixtures and Luminaires: Annual Sales Estimates and Projections in US$ Million by Product Segment for the Period 2020-2027 Table 134: Lighting Fixtures and Luminaires Market in Rest of Asia-Pacific: Historic Sales Analysis in US$ Million by Product Segment for the Period 2012-2019 Table 135: Rest of Asia-Pacific Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 136: Rest of Asia-Pacific Demand Estimates and Forecasts for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 137: Rest of Asia-Pacific Lighting Fixtures and Luminaires Market in US$ Million by End-Use: 2012-2019 Table 138: Lighting Fixtures and Luminaires Market Share Shift in Rest of Asia-Pacific by End-Use: 2012 VS 2020 VS 2027 LATIN AMERICA Table 139: Latin American Lighting Fixtures and Luminaires Market Trends by Region/Country in US$ Million: 2020-2027 Table 140: Lighting Fixtures and Luminaires Market in Latin America in US$ Million by Region/Country: A Historic Perspective for the Period 2012-2019 Table 141: Latin American Lighting Fixtures and Luminaires Market Percentage Breakdown of Sales by Region/Country: 2012, 2020, and 2027 Table 142: Latin American Lighting Fixtures and Luminaires Market Growth Prospects in US$ Million by Product Segment for the Period 2020-2027 Table 143: Lighting Fixtures and Luminaires Historic Market Analysis in Latin America in US$ Million by Product Segment: 2012-2019 Table 144: Latin American Lighting Fixtures and Luminaires Market by Product Segment: Percentage Breakdown of Sales for 2012, 2020, and 2027 Table 145: Latin American Demand for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 146: Lighting Fixtures and Luminaires Market Review in Latin America in US$ Million by End-Use: 2012-2019 Table 147: Latin American Lighting Fixtures and Luminaires Market Share Breakdown by End-Use: 2012 VS 2020 VS 2027 ARGENTINA Table 148: Argentinean Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2020-2027 Table 149: Lighting Fixtures and Luminaires Market in Argentina in US$ Million by Product Segment: A Historic Review for the Period 2012-2019 Table 150: Argentinean Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 151: Argentinean Lighting Fixtures and Luminaires Addressable Market Opportunity in US$ Million by End-Use: 2020-2027 Table 152: Lighting Fixtures and Luminaires Market in Argentina: Summarization of Historic Demand in US$ Million by End-Use for the Period 2012-2019 Table 153: Argentinean Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 BRAZIL Table 154: Lighting Fixtures and Luminaires Market in Brazil by Product Segment: Estimates and Projections in US$ Million for the Period 2020-2027 Table 155: Brazilian Lighting Fixtures and Luminaires Historic Market Scenario in US$ Million by Product Segment: 2012-2019 Table 156: Brazilian Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 157: Lighting Fixtures and Luminaires Quantitative Demand Analysis in Brazil in US$ Million by End-Use: 2020-2027 Table 158: Brazilian Lighting Fixtures and Luminaires Historic Market Review in US$ Million by End-Use: 2012-2019 Table 159: Brazilian Lighting Fixtures and Luminaires Market Share Analysis: A 17-Year Perspective by End-Use for 2012, 2020, and 2027 MEXICO Table 160: Lighting Fixtures and Luminaires Market in Mexico: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 161: Mexican Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 162: Mexican Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 163: Lighting Fixtures and Luminaires Market in Mexico: Annual Sales Estimates and Forecasts in US$ Million by End-Use for the Period 2020-2027 Table 164: Mexican Lighting Fixtures and Luminaires Market in Retrospect in US$ Million by End-Use: 2012-2019 Table 165: Lighting Fixtures and Luminaires Market Share Distribution in Mexico by End-Use: 2012 VS 2020 VS 2027 REST OF LATIN AMERICA Table 166: Rest of Latin America Lighting Fixtures and Luminaires Market Estimates and Projections in US$ Million by Product Segment: 2020 to 2027 Table 167: Lighting Fixtures and Luminaires Market in Rest of Latin America by Product Segment: A Historic Review in US$ Million for 2012-2019 Table 168: Rest of Latin America Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 169: Rest of Latin America Lighting Fixtures and Luminaires Latent Demand Forecasts in US$ Million by End-Use: 2020 to 2027 Table 170: Lighting Fixtures and Luminaires Historic Demand Patterns in Rest of Latin America by End-Use in US$ Million for 2012-2019 Table 171: Lighting Fixtures and Luminaires Market Share Breakdown in Rest of Latin America by End-Use: 2012 VS 2020 VS 2027 MIDDLE EAST Table 172: The Middle East Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Region/Country: 2020-2027 Table 173: Lighting Fixtures and Luminaires Market in the Middle East by Region/Country in US$ Million: 2012-2019 Table 174: The Middle East Lighting Fixtures and Luminaires Market Share Breakdown by Region/Country: 2012, 2020, and 2027 Table 175: The Middle East Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2020 to 2027 Table 176: The Middle East Lighting Fixtures and Luminaires Historic Market by Product Segment in US$ Million: 2012-2019 Table 177: Lighting Fixtures and Luminaires Market in the Middle East: Percentage Share Breakdown of Sales by Product Segment for 2012,2020, and 2027 Table 178: The Middle East Lighting Fixtures and Luminaires Market Quantitative Demand Analysis in US$ Million by End-Use: 2020 to 2027 Table 179: Lighting Fixtures and Luminaires Market in the Middle East: Summarization of Historic Demand Patterns in US$ Million by End-Use for 2012-2019 Table 180: The Middle East Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 IRAN Table 181: Iranian Market for Lighting Fixtures and Luminaires: Annual Sales Estimates and Projections in US$ Million by Product Segment for the Period 2020-2027 Table 182: Lighting Fixtures and Luminaires Market in Iran: Historic Sales Analysis in US$ Million by Product Segment for the Period 2012-2019 Table 183: Iranian Lighting Fixtures and Luminaires Market Share Analysis by Product Segment: 2012 VS 2020 VS 2027 Table 184: Iranian Demand Estimates and Forecasts for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 185: Iranian Lighting Fixtures and Luminaires Market in US$ Million by End-Use: 2012-2019 Table 186: Lighting Fixtures and Luminaires Market Share Shift in Iran by End-Use: 2012 VS 2020 VS 2027 ISRAEL Table 187: Israeli Lighting Fixtures and Luminaires Market Estimates and Forecasts in US$ Million by Product Segment: 2020-2027 Table 188: Lighting Fixtures and Luminaires Market in Israel in US$ Million by Product Segment: A Historic Review for the Period 2012-2019 Table 189: Israeli Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 190: Israeli Lighting Fixtures and Luminaires Addressable Market Opportunity in US$ Million by End-Use: 2020-2027 Table 191: Lighting Fixtures and Luminaires Market in Israel: Summarization of Historic Demand in US$ Million by End-Use for the Period 2012-2019 Table 192: Israeli Lighting Fixtures and Luminaires Market Share Analysis by End-Use: 2012 VS 2020 VS 2027 SAUDI ARABIA Table 193: Saudi Arabian Lighting Fixtures and Luminaires Market Growth Prospects in US$ Million by Product Segment for the Period 2020-2027 Table 194: Lighting Fixtures and Luminaires Historic Market Analysis in Saudi Arabia in US$ Million by Product Segment: 2012-2019 Table 195: Saudi Arabian Lighting Fixtures and Luminaires Market by Product Segment: Percentage Breakdown of Sales for 2012, 2020, and 2027 Table 196: Saudi Arabian Demand for Lighting Fixtures and Luminaires in US$ Million by End-Use: 2020 to 2027 Table 197: Lighting Fixtures and Luminaires Market Review in Saudi Arabia in US$ Million by End-Use: 2012-2019 Table 198: Saudi Arabian Lighting Fixtures and Luminaires Market Share Breakdown by End-Use: 2012 VS 2020 VS 2027 UNITED ARAB EMIRATES Table 199: Lighting Fixtures and Luminaires Market in the United Arab Emirates: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 200: United Arab Emirates Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 201: Lighting Fixtures and Luminaires Market Share Distribution in United Arab Emirates by Product Segment: 2VS 2020 VS 2027 Table 202: Lighting Fixtures and Luminaires Market in the United Arab Emirates: Recent Past, Current and Future Analysis in US$ Million by End-Use for the Period 2020-2027 Table 203: United Arab Emirates Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by End-Use: 2012-2019 Table 204: Lighting Fixtures and Luminaires Market Share Distribution in United Arab Emirates by End-Use: 2012 VS 2VS 2027 REST OF MIDDLE EAST Table 205: Lighting Fixtures and Luminaires Market in Rest of Middle East: Recent Past, Current and Future Analysis in US$ Million by Product Segment for the Period 2020-2027 Table 206: Rest of Middle East Lighting Fixtures and Luminaires Historic Market Analysis in US$ Million by Product Segment: 2012-2019 Table 207: Rest of Middle East Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 208: Lighting Fixtures and Luminaires Market in Rest of Middle East: Annual Sales Estimates and Forecasts in US$ Million by End-Use for the Period 2020-2027 Table 209: Rest of Middle East Lighting Fixtures and Luminaires Market in Retrospect in US$ Million by End-Use: 2012-2019 Table 210: Lighting Fixtures and Luminaires Market Share Distribution in Rest of Middle East by End-Use: 2012 VS 2020 VS 2027 AFRICA Table 211: African Lighting Fixtures and Luminaires Market Estimates and Projections in US$ Million by Product Segment: 2020 to 2027 Table 212: Lighting Fixtures and Luminaires Market in Africa by Product Segment: A Historic Review in US$ Million for 2012-2019 Table 213: African Lighting Fixtures and Luminaires Market Share Breakdown by Product Segment: 2012 VS 2020 VS 2027 Table 214: African Lighting Fixtures and Luminaires Latent Demand Forecasts in US$ Million by End-Use: 2020 to 2027 Table 215: Lighting Fixtures and Luminaires Historic Demand Patterns in Africa by End-Use in US$ Million for 2012-2019 Table 216: Lighting Fixtures and Luminaires Market Share Breakdown in Africa by End-Use: 2012 VS 2020 VS 2027

IV. COMPETITION Total Companies Profiled: 337 Read the full report: https://www.reportlinker.com/p02438202/?utm_source=PRN

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Amid the COVID-19 crisis and the looming economic recession, the Lighting Fixtures and Luminaires market worldwide will grow by a projected US$25.6...

A perfect mix of quantitative & qualitative LED Secondary Opticmarket information highlighting developments, industry challenges that competitors are facing along with gaps and opportunities available and would trend in LED Secondary Opticmarket. The study bridges the historical data from 2014 to 2019 and estimated until 2025.

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DUBLIN--(BUSINESS WIRE)--The "Global Smart Lighting Market Analysis 2020" report has been added to ResearchAndMarkets.com's offering.

The Global Smart Lighting market is expected to reach $40.02 billion by 2026 growing at a CAGR of 19.5% during 2018 to 2026. A smart lighting system is utilized to reduce greenhouse emissions by means of inert infrared and tenancy sensors. There are wired and non-wired smart lighting systems that are used for the power-saving function in the residential, commercial, and industrial sectors.

Factors such as increasing acceptance of Li-Fi technology, mounting number of stylish city development initiatives across the globe and growing demand for energy efficient lighting are driving the market growth. Though, short of installation and payback awareness is restraining the market. Development in lighting control is the opportunity for the Smart Lighting market.

Based on end user, outdoor segment is anticipated to grow at the significant rate during the forecast period, due to the mounting focal point on enhancing connectivity by constructing roads and installing smart lights intended for these roads, particularly in the rising countries in the APAC region. This, in turn would spur the development of the outdoor market.

Companies Mentioned

Key Questions Answered in this Report:

Key Topics Covered:

1 Market Synopsis

2 Research Outline

2.1 Research Snapshot

2.2 Research Methodology

2.3 Research Sources

2.3.1 Primary Research Sources

2.3.2 Secondary Research Sources

3 Market Dynamics

3.1 Drivers

3.2 Restraints

4 Market Environment

4.1 Bargaining power of suppliers

4.2 Bargaining power of buyers

4.3 Threat of substitutes

4.4 Threat of new entrants

4.5 Competitive rivalry

5 Global Smart Lighting Market, By Installation Type

5.1 Introduction

5.2 Retrofit Installations

5.3 New Installations

6 Global Smart Lighting Market, By Offering

6.1 Introduction

6.2 Software

6.3 Services

6.4 Hardware

7 Global Smart Lighting Market, By Lighting Technology

7.1 Introduction

7.2 Light Emitting Diode

7.3 Incadescent

7.4 Fluorescent

7.5 Halogen

7.6 High Intensity Discharge Lamps

7.7 Neon Lighting

8 Global Smart Lighting Market, By Communication Technology

8.1 Introduction

8.2 Wireless

8.3 Wired Technology

9 Global Smart Lighting Market, By End User

9.1 Introduction

9.2 Outdoor

9.3 Indoor

10 Global Smart Lighting Market, By Geography

10.1 Introduction

10.2 North America

10.3 Europe

10.4 Asia Pacific

10.5 South America

10.6 Middle East & Africa

11 Strategic Benchmarking

12 Vendors Landscape

For more information about this report visit https://www.researchandmarkets.com/r/kzknwm

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Outlook on the Worldwide Smart Lighting Industry to 2026 - Key Drivers and Restraints - ResearchAndMarkets.com - Business Wire

The Global Smart Lighting Market 2020 Research Report is a professional and detailed study about the current and forecast state With COVID 19 Impact Analysis of the market.

MarketResearch.Biz offers a 360-degree view of the global Smart Lighting market and offers accurate forecasting and also covers competitive landscapes, with in-depth market segmentation including type segment, industry segment, channel segment etc., vital trends and strategic recommendations to enable our clients. It also includes different clients data, which is very crucial for the manufacturers.

The historical information of the global Smart Lighting market and evaluate the present market scenario based on the key factors determining the trajectory of this Smart Lighting market with the help of primary and secondary data, the Smart Lighting market research report projects the future and makes valid prediction. Moreover, the Smart Lighting industry research report also incorporates insightful information from industry specialists to uplift readers to make well-informed business desicion. The Smart Lighting market report also uses SWOT analysis and Porters five forces analysis to shed light on the important elements of the Smart Lighting Market.

CLICK HERE !! To Know What Is The Impact Of COVID 19 On CurrentSmart Lighting Market And How Market Will Grow In The Upcoming Period 2020-2029?

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The report includes an reckoned impact of strict standards and regulations set by the government over the Smart Lighting market in the forecast years. The market report also includes thorough research done using several analytical techniques such as SWOT analysis to identify the market growth pattern.

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Brief Introduction to the research report.

Table of Contents (Scope covered as a part of the study)

Top players in the market

Research framework (structure of the report)

Research methodology adopted by MarketResearch.Biz

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Major Players Are:

Zumtobel Group, Lutron Electronics Company Inc., Digital Lumens, StreetLight Vision, Legrand S.A., Philips Lighting, Honeywell International Inc, OSRAM Licht AG, General Electric Company, Acuity Brands Lighting Inc. and Inc.

Market Segmentation:

Global smart lighting market segmentation by component: Relays, Controllable breakers, Sensors, Switch actuators, Dimmer actuators. Global smart lighting market segmentation by light source: LED light source, Fluorescent light source, Compact fluorescent light source, High intensity and discharge light source. Global smart lighting market segmentation by product type: Smart bulbs, Fixtures, Lighting control. Global smart lighting market segmentation by application: Commercial & industrial, Residential, Outdoor lighting, Indoor lighting, Public & government buildings, Others

Regions & Countries Mentioned In The Smart Lighting Market Report:

North America ( United States)

Europe ( Germany, France, UK)

Asia-Pacific ( China, Japan, India)

Latin America ( Brazil)

The Middle East & Africa

Some of the questions related to the Smart Lighting market addressed in the report are:

With the developing demand, how are market players aligning their activities to fulfill the demand?

Which place has the most favorable regulatory rules to conduct commercial enterprise in the present Smart Lighting market?

How has technological advances inspired the Smart Lighting market?

At present, which organization has the very best market share in the Smart Lighting market?

What is the maximum lucrative income and distribution channel used by market players in the worldwide Smart Lighting market?

The market study bifurcates the worldwide Smart Lighting market on the basis of product type, regions, application, and end-user industry. The insights are backed with the aid of accurate and easy to understand graphs, tables, and figures.

Role of Smart Lighting Market Report:

Save and decrease time sporting out entry-level analysis by evaluatng the growth, size, key players and segments in the global Smart Lighting Market.

Highlights vital business priorities in order to help companies to realign their business strategies.

The crucial findings and recommendations focuses key progressive industry trends in the Smart Lighting Market, thereby allowing players to build effective long term strategies.

Develop/modify business growth plans by using substantial expansion offering developed and emerging markets.

Scrutinize detailed worldwide market trends andoverview coupled with the factors driving the market, as well as those inhibit it.

Intensify the decision-making process by understanding the strategies that support commercial interest with admire to merchandise, segmentation and industry verticals.

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Table of Contents

Outlook of the Smart Lighting Market: This section covers the key manufacturers, market segments, study aim and analysis of market size for the 2020-2029 forecast period.

Presumption and Growth Trends Highlighted until 2029: This prospects based on 3 section such as growth rate of key producers, industry trends, and manufacturing estimation.

Smart Lighting Player Market Share: This consist player production, revenue, and price calculation at the side of other chapters, such as growth plans and mergers and acquisitions, products include with the aid of top players and served areas and headquarters distribution.

Market size: Size of the market includes analysis of price, market share of the production value and market share of production.

Company profiles: Here, new entrants and leading players in the Smart Lighting market are analysed based on gross margin, revenue, sales area, vital products, price and production.

Smart Lighting Analysis of the market value chain and sales channel: Covering analysis of distributor, value chain, customer and sales channel.

Market forecast: In this part of the report, the analyst have targeted on the forecast of the value of production, the forecast of consumption by region, the forecast of production by region, the forecast of manufacturing and earnings and the regional forecast.

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COVID 19 Impact Estimates On Smart Lighting Market 2020-2029 | Professional And Detailed Study By MarketResearch.Biz - Cole of Duty

Yes, dice will roll, cards will be dealt and slot machines will beckon. But poker rooms? Closed. Tourists returning to Las Vegas will see changes since gambling stopped in mid-March because of the coronavirus pandemic. (May 22) AP Entertainment

LAS VEGAS As resortsprepare to reopen in the era of COVID-19, it's a burning question:Shouldsmoking be banned inside Las Vegascasinos?

The Las Vegas Strip has long been a destination for people looking to get away from the rules of home, gamble into the night and freely puffcigarettesindoors, reports the Reno Gazette Journal, which is part of the USA TODAY Network..

In the wake of the deadlyand contagious coronavirus respiratory illness that's killed more than 100,000 people across the country, smoking inside casinos has resurfaced as a make-or-break detail for tourists planning post-pandemic vacations.

"I personally will never step foot in a casino again if they allow people to continue to smoke while claiming they are doing everything they can to protect people's health," Debbie Ellis wrote in an emailto Nevada's Gaming Control Board.

"This is just common sense," wrote Susan Lang. "Smoking must be banned on the casino floor. Speak to any health official. They would agree."

But banning smoking in Nevada casinos is not up to gaming authorities.

Lighting upat card tables and slot machines is protected by Nevada law.

How does coronavirus enter the body, and why does it become fatal for some compared to just a cough or fever for others? USA TODAY

Though the Silver StatelaunchedtheNevadaClean Indoor Air Act in 2006, the lawdoesn't quite apply to The Strip.

The law outlines where people can smoke indoors inNevada, as opposed to where they can't andthe list where smoking is acceptable is a long one:

Banning smoking in casinos is a legislative issue that would have to be addressed when statelawmakers meetagain in 2021, accordingto Nevada Chief Deputy Attorney General Darlene Caruso.

"The legislature has specifically stated that smoking is not prohibited in casinos, and the statute even includes a provision that states any regulation inconsistent with that would be null and void," Caruso said at the Gaming Control Board's Tuesday meeting."The boards hands are tied."

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As hotel-casinos prepare to open on The Strip June 4, the Nevada Resort Association contends all properties are in full compliance with theNevada Clean Indoor Air Act.

"Our members have always placed a high priority on air quality and have invested heavily in advanced technology throughout their properties that circulates fresh air and removes smoke and odors," theresortassociation said in a statement. "They have reviewed their HVAC systems and are enacting additional measures to maximize the exchange of fresh air and are increasing the frequency of air filter replacement and system cleaning."

Dr. Meilan Han, a pulmonary specialist at Michigan Medicineand professor at the University of Michigan, told USA TODAYthat while most of the research about the novel coronavirus suggests older people are more likely to be hospitalized and die of the disease, there are other factors that put younger people at risk.

"People have been hypothesizing as to what some of the risk factors might be. We don't have a lot of published data from the U.S., so we're looking to the little bits of published data that are coming out of China,"Han said."What they're seeing is that one of the risk factors ...does appear to be smoking."

One report suggests that smokers have a 14-times higher risk of severe illness with a COVID-19 infection than nonsmokers, she said.

"We don't have a lot of data on vaping right now, but there is reason topotentially hypothesize that things that cause lung inflammation like smoking, like vaping might increase the risk for more severe disease," she said.

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Nevada casino executives have long arguedthat a casino smoking ban would derailgaming in the Silver State, pointingto losses of gaming revenue in Atlantic City, Delaware and Illinois, where such bans have siphoned the smoke out ofcasinos and gaming establishments.

"Historically, other jurisdictions that have banned smoking in casinos have seen revenues fall by about 15% in the short term," said University of Nevada, Las Vegas gambling expert and author David Schwartz.

A study commissioned by the Las Vegas and Reno-Sparks chambers of commerce in the late 1990s predicted dire consequences if smoking becamebanned in casinos, according to the Associated Press.

Gamblers would leave tables for 12 minutes every hour to light up, the study said.Those smoke breaks would add up to significantlosses:

But it may soon be imperative to pushrevenue projections aside and reconsider their support of smoking, according to Schwartz.

"I can't speak to the science around whether smoking is a factor in the spread of infectious disease, but to the extent that visitors may believe that it is a factor, casinos may want to reconsider whetherthey allow smoking indoors," Schwartz said in an email.

"Coming back from COVID-19, both casino patrons and employees may have increased concerns about the presence of cigarette smoke," he said."Given that the customer experience will be very different when casinos reopen, this may be an ideal time for Nevada gaming operators to consider changing where they allow smoking on their properties."

Contributing: USA TODAY, Associated Press.

Ed Komenda writes about Las Vegas for the Reno Gazette Journal and USA Today Network.

Read or Share this story: https://www.usatoday.com/story/travel/news/2020/05/28/las-vegas-opening-covid-19-smoking-ban-unlikely-coronavirus/5277393002/

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Las Vegas opening from COVID-19 with a smoking ban? Don't count on it - USA TODAY

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CP Photo: Jordan Snowden

A few of Jordan Snowden's indoor plants.

My plant-mom status has evolved into a plant smother. I find myself checking in on my plants every day: stroking their leaves, touching their soil, moving them into spots of light when the sun emerges from behind a cloud, playing Mort Garsons Mother Earth's Plantasia as loud as I can without disturbing my neighbors. Ive been taking care of them, but really, they are taking care of me. And it turns out, this is something thats not unique to me or the current circumstances.

Biophilia is a term thrown around quite often, but what does it truly mean, or better yet what positive effects can it have? asks Tom Horowitz, vice president of Plantscape in the Strip District. The Well International WELL Building Institute concurs that humans have a psychological affinity toward nature, the natural world, and its simple processes. Just being around nature, or even a photograph of nature, can help boost our mood and give us those warm fuzzies.

Beyond that, The Attention Restoration Theory explains that working within or in my case, living and working in environments with elements of nature can aid in the restoration of mental capacity when dealing with demanding tasks and/or distracting environmental factors that lead to mental fatigue.

Within our post-COVID-19 world, our bodies are in a state of fight-or-flight, says Horowitz. Adding interior plant material has shown to provide a sense of safety within the subconscious part of the brain. Biophilia also, from a physical health perspective, provides positive outcomes. The NIH publication by Grinde and Patil states that a decrease in health complaints, such as tiredness and coughing, has been reported in office and hospital workers when plants were added to the work environment.

The aesthetic of a space full of plants has also been tantalizing while stuck at home. Being in my apartment almost 24/7 has made me want to spruce up my living situation. And since the start of the quarantine, Ive added about eight new plant babies to my collection.

A home or office can sometimes feel a bit lifeless without plants, says Abi Falcioni, owner of Perrico Plant Co. They are a relatively inexpensive way to add color and life into a space. In addition, it's nice to just nurture and care for something once in a while, and plants are an easy way to fulfill that desire.

Drew Clouse of City Grows backed Falcioni up, adding, Plants can add unique pops of color in the home and help fill rooms [that have] unwanted empty space. But he warned about the importance of picking the right plants to suit your home and lifestyle.

Taking care of a plant, while not as daunting as a cat or dog, is still taking care of a living thing, and figuring out the right care routine for them is important, says Clouse. Having a designated time to water them, whether it's weekly or monthly, or to otherwise check on them daily helps to keep to a schedule for many people.

Also plants provide a unique learning opportunity. Most people might not realize that taking care of plants, even for seasoned people, can be difficult at times, and that even we might wind up with some brown leaves or run into other issues, but that's part of the process of having plants.

As plants grow in your home, you grow with them. You learn about them, and in doing so learn about yourself. Do you have the patience for daily watering? Or are you a once-a-week type person? Will you propagate your plants to share with others? Or propagate them to make your collection an indoor jungle?

CP photo: Jared Wickerham

Abi Falcioni, owner of Perrico Plant Co., inside her Lawrenceville shop

Plus, as Horowitz pointed out, They do not have any current restrictions or need to wear masks! Plants are powerful in so many ways especially in todays climate.

Below, Pittsburgh City Paper chatted with a few local plant sellers to get some tips and advice on being a great plant parent.

We will enforce social distancing practices and suggest masks for our customers, says Brittain, of Shadyside Nursery. If customer numbers climb extensively, we will limit the number of guests and we have talked about doing a senior/sensitive group hour for shopping in the a.m. if needed. Keeping our community and our team safe and healthy is our priority and we will be adapting as needed to do so.

Shadyside Nursery will be open Mon.-Fri., 10 a.m.-7 p.m. and 9 a.m.-9 p.m. on weekends.

We are incredibly grateful to our Pittsburgh community that has supported us by shopping on our online store during this time, says Brittain. Let the victory gardens and jungalows commence!

Recommendations for beginners: Philodendron, ficus (rubber), pothos, and our all-time favorite [is] sansevierias (snake plant). COVID-19 has created an interesting dynamic in the industry and the silver lining for us is that some hard-to-source varieties became available to us. We are very excited to be a source of improving your home with houseplants and offer them to our Pittsburgh community.

Intermediate recommendations: Cathatea, hoya, ficus tree, fiddle leaf fig, and the lipstick plant

Most unique plant in stock:Our most popular plant is the carnivorous pitcher plant. It's AMAZING, it's weird, and it eats fruit flies, so it's the real show-stopper out of the group. We have 10" sansevieria sayuri that we are thrilled to have in stock. As sansevieria collectors, this is the first time to see this variety in person or have any size in stock at the shop! We also have some notable hoya and lipstick plant hanging baskets, as well as the sought after cereus peruvianus double cactus.

A typical plant mistake you see which can easily be fixed:Identifying each plants needs and acting accordingly is very important (water, light, soil). I teach multiple classes at Workshop PGH that specialize in plants and the most notable is Houseplants 101 - How Not to Kill Your Plants. The class empowers people to work with plants, care for them inside and outside of their home, and remember that in doing so, it's gratifying and can also be humbling. The most common mistake is when someone kills a couple of plants, they think they have a "black thumb" or are incapable of caring for plants in the future, which is absurd. Anyone can stop in at the nursery and gather tips and recommendations on how to be successful!

Fortunately, we had a great response from Pittsburgh customers who were willing to pay for shipping because they wanted to shop local, so itll be great to be able to safely serve them with curbside pickup from now on, says Perrico Plant Co. owner Abi Falcioni.

Falcioni recently left her corporate job to pursue running Perrico full time and takes a great deal of pride in being a female small business owner.

I have thoroughly enjoyed the challenges and rewards of delivering plant happiness to all my amazing customers, says Falcioni. Additionally, the brick-and-mortar location has allowed me to expand to wholesale, giving me the opportunity to support other local plant businesses.

Falcioni also runs a YouTube channel (Perrico Plant Co.) where she gives a behind-the-scenes look at operating a house plant business, as well as advice about house plants.

Recommendations for beginners: Our entire website is dedicated to helping beginner house plant owners (or black thumbs) find their perfect plant. Our favorites for beginners are ZZ plants and snake plants (sansevieria), as well as easy vining plants like a pothos or philodendron. We actually offer a Beginner Bundle [with] three beginner plants which allows customers to get a great deal on a few plants that will be easy to care for.

Intermediate recommendations: We call these "graduation" plants and would recommend a stromanthe triostar to anyone looking for a striking plant but is willing to put in the effort to keep it consistently watered and provide a humid environment, if possible.

Most unique plant in stock: Since we focus on plants that are great for beginners, we don't tend to carry many rare plants, but a fan favorite is the monstera adansonii which is a skinnier cousin to the monstera deliciosa (Swiss cheese plant) that has gained in popularity over the last few years.

A typical plant mistake that you see which can easily be fixed:A typical problem is not picking a plant that will suit your space. We have our website designed to take customers through a few questions related to lighting and watering that will help them narrow the choice down to the plants that will work best in their home. Too often people pick a plant that might look interesting, but they don't have the right conditions to keep the plant happy. We try to guide customers to plants that will have a good chance of thriving in the space.

Recommendations for beginners:For beginners, we recommend sticking to low- to medium-light plants, such as a cast iron, sansevieria, pothos, and aglaonemas. We have a plant selection guide on our website (plantscape.com/plant-selection-guide.htm) for reference.

Intermediate recommendations: More advanced green thumbs could attempt a bonsai or other exotic varieties if they have the proper light levels.

Most unique plant in stock: I would say the aglaonema with its vast selection in varieties. They range from dark green to speckled, to shades of pink or red. But if you are looking for a statement, a pencil cactus (Euphorbia tirucalli) is the way to go.

A typical plant mistake that you see which can easily be fixed:Under- or over-watering tends to be the biggest mistake made when caring for plants. Giving little drinks of water here and there doesnt get down and saturate the roots, and using too much water or watering too frequently the roots will rot since they dont have a chance to dry out. Pro-tip is to water thoroughly so water comes out the drainage hole after a while, empty the saucer or container of any sitting water and then let the soil dry down several inches or more before watering again. Better to under-water than over-water! The other mistake is not matching the plant to the light levels. The plant selection guide on our website shows light levels required.

In addition to plants, City Grows sells a variety of pottery for plants and plants for organic urban gardening. It also has seeds, seedlings, soil, and various fertilizers and other soil amendments.

Photo: Natasha BrittainCaption

Plants available at Shadyside Nursery

Intermediate recommendations: Our calathea and maranta are probably in that range. They need a bit more care: mainly higher humidity, which can be achieved by misting the plants or putting a tray with rocks or pebbles at the bottom, or a humidifier if you want to make the investment. They also need watered every 5-7 days generally, but do well in low-medium light.

Most unique plant in stock: Our most unique plant is probably the Buddhist pine. It's a conifer native to Japan and China and also makes a great houseplant for our region.

A typical plant mistake that you see which can easily be fixed:People tend to over-water succulents and cacti, or they don't water them at all, when they need water every 3-4 weeks. I usually try to tell people to try to get as close as possible to replicating any plants natural environment.

My brother Brian helps us when we are busy, and my other brothers mother-in-law Dianne helps us water and plant flowers, herbs, and vegetables, says Andy.

Schweikert Greenhouse is open to the public, as the Schweikerts have seven spaced out greenhouses. They require a mask in accordance with Governor Wolfs order, but customers can come and shop while maintaining social distancing. Schweikert specializes in flowers, herbs, vegetable plants, perennials, and succulents.

Most of our plants are for beautifying your home or to grow your own vegetables or herbs in your garden, says Andy. For houseplants, we mainly have cactus and succulents which my 93-year-old grandma Betty Schweikert has grown and cared for over the years.

Recommendations for beginners:I would recommend solenia begonias. They can grow in sun or part sun to shade and dont require a lot of water. Another easy plant for beginners is vinca. They love very hot sunny weather and are extremely drought tolerant.

Intermediate recommendations: For a hanging basket I recommend a mandevilla or a dipladenia, as they do wonderful in hot weather and continuously bloom, and do not need watered every day.

Most unique plant in stock: Our most unique plant is a customer favorite of ours, a cherry tomato basket. The variety of tomato is specially bred to grow in a basket, and they produce hundreds of tomatoes all summer. They just take a lot of water because they get very large throughout the summer. Another very unique plant we have is a succulent called a lifesaver cactus; they produce a yellow and red bloom that is amazing.

A typical plant mistake that you see which can easily be fixed:The biggest mistake is choosing the wrong place for a plant meaning putting a plant that likes shade in a sunny location or over- or under-watering. Some plants love lots of water while others will do poorly with lots of water, so knowing the plant's requirement is important.

More here:
How to grow your happiness with indoor foliage - PGH City Paper