By: Express News Service | Chandigarh | Published: May 31, 2020 2:13:58 am Punjab chief minister Amarinder Singh

Rejecting allegations of withdrawal of free power to Punjabs farmers, Chief Minister Amarinder Singh on Saturday said his government is ready to forego the portion of fiscal deficit enhancement offered by the Centre but would not compromise with the farmers interest at any cost.

Dismissing the Centres suggestion on replacement of free power to farmers with DBT as totally unacceptable and a direct attack on the federal structure of the nation, the Chief Minister said he will take up the issue with the Centre for trying to impose such an anti-farmer condition on the cash-strapped state in the guise of extending fiscal support amid the Covid pandemic.

Amarinders statement came a few days after his Cabinet cleared the proposal of Centre to enhance the borrowing limit if state forgoes free power to farmers in the state. The Opposition then took on the government and Amarinder announced on Friday that free power would continue.

Asserting that the free power facility for farmers would continue till his government is in power, Amarinder said his government will take loans to bridge the fiscal deficit, and the Government of India cannot dictate the terms of a sovereign loan being taken by a state government.

Amarinder also lashed out at Shiromani Akali Dal (SAD) chief Sukhbir Badal for trying to pin the blame for the Central governments misdemeanors on the state government, which had unwaveringly been providing free power to farmers since taking over in March 2017, despite the massive fiscal crunch it had inherited from the erstwhile Akali-BJP regime.

The Chief Minister asked Sukhbir to immediately quit the ruling NDA at the Centre, and demanded Union Minister and Akali leader Harsimrat Kaur Badals resignation from the Union Cabinet, pointing out that it was the Government of India that took the decision directing the states to withdraw the free power, and also failed to come to the rescue of the state, and any section of its people, in the wake of the unprecedented Covid crisis and lockdown.

Terming their allegations malicious and mischievous, and totally unsubstantiated, Amarinder hit out at the SAD leadership for not fighting for the rights of Punjab and its people, including farmers, at the Centre or in Parliament, and resorting, instead, to petty and shameless politicking even on such a grave issue of national concern.

If you have even an iota of shame, you should leave the NDA coalition, of which you are a part, immediately, and join my government in working for the people of the state, the Chief Minister said, criticising Sukhbir and Harsimrat over their pathetic attempts to mislead the people of Punjab with their patent double standards, blatant falsehoods and unfounded allegations.

Amarinder clarified that the state Cabinet had, in its last meeting, given an in-principle approval to undertaking certain reforms to become eligible to avail additional borrowing of 1.5% of Gross State Domestic Product (GSDP) amid COVID-19, as mandated by the Government of India. While allowing an enhancement of fiscal deficit of states under FRBM Act from 3% to 5%, the Government of India had linked a portion of the same to certain administrative reforms.

It is for the BJP and its alliance partner SAD to explain why there is an attempt to force such a condition on Punjab, he said.

Earlier, during the day Panchayats and Rural Development Minister Tript Rajinder Singh Bajwa also came up to defend his government on the issue. Showing the Centres letter asking state to do away with subsidy, Bajwa asked Harsimrat to resign from Modis Cabinet or get the state an unconditional enhancement of borrowing limit.

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See original here:
Punjab farmers will get free power till I am in power: CM - The Indian Express

An artist's impression of what the new Toll Bar Roundabout will look like has finally been revealed, as work is due to start next month.

North East Lincolnshire Council are due to carry out a major redevelopment of the controversial roundabout, which will see additional lanes added to it in an effort to improve traffic flow.

The work on the roundabout is due to begin in July and will last for five months.

The local authority is looking for contractors to do it.

The redevelopment of the Toll Bar Roundabout has been a highly debated issue in North East Lincolnshire for a number of years after plans were originally drawn up to remove it and replace it with traffic lights.

These plans were backed by the approval of a huge 400 home housing development on land beside the roundabout, which demanded the replacement of the traffic lights.

This decision sparked a long series of protests by local residents, who formed the Toll Bar Residents Action Group and had weekly demonstrations at the junction.

However this decision was reversed after the Conservative Party took control of the council in last year's local elections. This was one of their key campaign pledges at the time.

Cabinet member for transport and the environment, Councillor Stewart Swinburn, said once the contractor is appointed, further details will be known on how traffic will be managed during the works programme.

He said: We are acutely aware of the potential issues to residents and businesses while the works are undertaken.

To that end, we have asked potential contractors to review options on ways to reduce the impact of the works on the local community and businesses affected by the works.

If residents or businesses have any specific concerns, they are encouraged to get in touch with the delivery team ahead of the works beginning in July, we will write to all nearby residents and businesses directly in the coming weeks to discuss any access arrangements.

The image we have released illustrates the significant changes being made while retaining the roundabout. Hopefully, once completed, residents and businesses will feel the disruption was worthwhile.

The new housing development has committed to plans to build a 33 space drop off point for the nearby Toll Bar Academy.

Councillor Swinburn said: Although the parking facility is subject to the housing development progress, we are already looking at ways to increase this capacity and improve road safety in the area.

"If were able to provide a proper parking facility, this will help us implement additional parking restrictions on the busy link road between New and Old Waltham to make sure pedestrians are as safe as possible."

The council has said that it will be engaging with local stakeholders ahead of the start of the redevleopment.

Residents or businesses with a specific issue regarding the project should contact the delivery team by email at Tollbarjunction@nelincs.gov.uk or write to Toll Bar Junction Improvements Engagement Team, New Oxford House, George Street, Grimsby, DN31 1HB.

See the original post here:
How the new Toll Bar Roundabout will look after five months of work - Grimsby Live

The Panther Creek bridge on KY 81 will not reopen until the entire structure can be replaced, according to the Kentucky Transportation Cabinet. A temporary bridge could be installed in the coming months to the East side of the structure.

The bridge was damaged last week by an oversized vehicle that impacted every crossmember along the top portion of the structure. A final analysis of damage to the bridge indicates that it will not reopen to traffic.

We are working closely with the Secretarys office in Frankfort to secure the funds necessary to replace the existing structure, said Chief District Engineer Deneatra Henderson. We are considering a design-build style procurement to allow us to expedite the process. Typically, projects of this nature tend to move a little faster.

A timeline to replace the structure is unknown at this time. The timeline for a temporary bridge to be opened to traffic over Panther Creek is roughly 8-10 weeks.

Before arriving on-site to examine the damage to the bridge, I had already reached out to a contractor with a military-style temporary structure, Henderson said. We were able to receive permission from the adjoining property owners to remove trees on the East side of the bridge, in preparation for the installation of the temporary structure.

While the damage is very similar to what occurred in August of 2019, the heat-straightening technique used to repair the bridge cannot be used again, as the steel is already brittle from previous repairs.

That was demonstrated during the new impact, with the evidence of cracking along the bolt holes at some of the beams. Because of this, engineers in the Kentucky Transportation Cabinet Bridge Division have determined that the bridge cannot reopen, and repairs would not be cost effective when compared to the cost of replacement.

Traffic on KY 81 is being routed along a marked detour on KY 554 and U.S. 431. Motorists should use appropriate caution along the detour, and larger vehicles should not attempt to shorten their route by making the turns with Todd Bridge Road. There are variable message boards along the route to encourage motorists to stay on the marked detour.

The impact occurred May 18 but was not discovered until May 21. The Kentucky State Police are still investigating the crash. Troopers are asking anyone who has a home or business security camera on KY 56 and KY 81 to check May 18 between the hours of 1-3 pm for footage containing the vehicle in the photos below.

Anyone who has additional pictures and/or can establish the identity of the owner/operator of the pictured vehicle, please contact KSP at 270-826-3312.

Opened to traffic in 1934, the 246-foot structure with a 109-foot main span includes a 6-panel Pratt through truss design. The Panther Creek Bridge underwent an extensive painting and maintenance project in the fall of 2018. In August of 2019, it was damaged by a truck carrying a load of metal and closed to traffic. The bridge reopened two days prior to Thanksgiving 2019. Approximately 5,200 vehicles cross the bridge each day.

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Panther Creek bridge will not reopen; structure must be replaced - The Owensboro Times

Buzz is growing about Sen. Elizabeth WarrenElizabeth WarrenCOVID-19 workplace complaints surge; unions rip administration Gloves come off as Democrats fight for House seat in California Police killing in Minneapolis puts new scrutiny on Biden pick MOREs interest in joining former Vice President Joe BidenJoe BidenBiden campaign cancels fundraiser with Mueller prosecutor Twitter joins Democrats to boost mail-in voting here's why The Hill's Morning Report - Presented by Facebook - George Floyd's death sparks protests, National Guard activation MORE atop the Democratic presidential ticket, but one of the biggest obstacles she faces is the possibility that her Senate seat could wind up getting filled by a Republican.

Warren (D-Mass.) is in regular contact with Biden, the presumptive Democratic presidential nominee, and plans to hold a big online fundraiser for him on June 15. The high-profile progressive has also increased her outreach to Bidens longtime Senate allies, sending the message that shes eager to join Team Biden, according to Senate sources.

Picking Warren could give Biden a boost in the polls. A Morning ConsultPolitico survey released Wednesday found that 26 percent of registered voters said they would be more likely to vote for Biden if he chose Warren, putting her ahead of the other eight women he is said to be considering.

Her greatest impact, according to the survey, would be among voters under the age of 45, as well as black and Hispanic voters three blocs Democrats see as key to winning the White House and picking up Senate seats.

But picking Warrenwould likelycost Democrats one of those Senate seats, at least temporarily, if Biden were to defeat President TrumpDonald John TrumpMinneapolis erupts for third night, as protests spread, Trump vows retaliation Stocks open mixed ahead of Trump briefing on China The island that can save America MORE.

Warren represents a state with a Republican governor, Charlie Baker, who has the power to pick anyone he wants to fill her seat, should it become vacant, until a special election sometime in 2021.

Massachusetts law requires a special election to be held between 145 and 160 days after a Senate seat becomes vacant, a significant problem if Biden wins the White House and the Senate is evenly divided between Republicans and Democrats.

If Baker were to appoint a Republican to replace Warren, Democrats would need a net pickup of four Senate seats instead of three and control of the White House to win back the Senate majority, where the GOP holds a 53-47 advantage.

Meanwhile, two other potential VP candidates Sens. Kamala HarrisKamala Devi HarrisMinneapolis erupts for third night, as protests spread, Trump vows retaliation The Hill's Morning Report - Presented by Facebook - George Floyd's death sparks protests, National Guard activation Police killing in Minneapolis puts new scrutiny on Biden pick MORE (D-Calif.) and Amy KlobucharAmy KlobucharPolice killing in Minneapolis puts new scrutiny on Biden pick Cortez Masto says she's not interested in being Biden VP Voting rights, public health officials roll out guidelines to protect voters from COVID-19 MORE (D-Minn.) represent states with Democratic governors, meaning they don't come with the same risk of a GOP successor.

Given how the Senate battleground map is shaping up, a 50-50 Senate tie counting both Massachusetts Senate seats in the Democratic column is becoming a distinct possibility.

How quickly a potential Baker appointee could be casting votes in the chamber would be determined by Warren.

If she were to resign on Nov. 4, the day after Election Day, that could shave off 61 days during which a temporary appointee would serve in the 117th Congress, assuming the Senate convenes on Jan. 4, the first Monday in January, as it often has.

That would still give a Republicansenator at least 84 days to serve in a new Senate session.

If one seat means the difference between three more months of GOP control, that could seriously blunt the first 100 days of a Biden administration, potentially delaying the confirmation of Cabinet nominees and the consideration of Democratic-backed legislation.

Some strategists say its a gamble worth taking.

To me its become increasingly clear that she would be the strongest pick electorally, said Mike Lux, a Democratic strategist who worked in the Clinton White House. Shes actually very strong with African American and Latino and Latina voters.

Lux said Warren also really increases the enthusiasm level, which is one of the key things the Biden folks need to figure out.

Theres this big enthusiasm gap in a lot of the polls between Biden and Trump, and I think Warren would really help change that dynamic, he added.

Warren herself appears enthusiastic about the VP possibility.

One Senate Democrat said her jockeying for the veep slot has been noticeable, while other Senate sources noted shes been reaching out more to Bidens allies on Capitol Hill.

This month she co-sponsored Sen. Christopher CoonsChristopher (Chris) Andrew CoonsVoting rights, public health officials roll out guidelines to protect voters from COVID-19 The Hill's Morning Report - Presented by Facebook - US virus deaths exceed 100,000; Pelosi pulls FISA bill Warren's VP bid faces obstacle: Her state's Republican governor MOREs (D-Del.) Pandemic Response and Opportunity Through National Service Act. The legislation would expand existing national service networks such as AmeriCorps to help respond to the coronavirus crisis.

She also co-sponsored a bill led by Sen. Bob CaseyRobert (Bob) Patrick CaseyWarren calls for investigation into OSHA inspections during pandemic The Hill's Morning Report - Presented by Facebook - US virus deaths exceed 100,000; Pelosi pulls FISA bill Warren's VP bid faces obstacle: Her state's Republican governor MORE Jr. (D-Pa.), another Biden ally, to better protect the elderly and people with disabilities living in nursing homes, intermediate care facilities and psychiatric hospitals from the pandemic.

Biden and Warren have been speaking regularly in recent weeks, about once a week, according to sources with knowledge of the phone calls.

Warren has also spoken to former President Obama, mostly about her ideas surrounding the pandemic.

In recent months, Obama has told those around him that he was impressed with the ideas she had on the campaign. Last fall, he went to bat for her when speaking to donors who were turned off by her politics, vouching for her political chops and her fluency on policy.

"With the economy in the gutter, I think he believes she can play a big role in the recovery," said one Obama ally. "He appreciates her ideas."

One Biden ally said theformer vice president also "appreciates and respects" Warren's ideas.

"She's at the top of the list for a reason," the ally said.

Liberal strategists who want to see Warren on the ticket say the overwhelmingly Democratic state Senate and House in Massachusetts could change the states law to require Baker to pick someone from the same party as Warren.

Democrats control 34 seats in the Massachusetts state Senate while Republicans hold four, with two vacancies. In the state House, Democrats hold a 126-31 advantage, with seats vacant.

Six states require the governor to appoint a replacement of the same party as the departing senator, according to a 2017 report by the National Conference of State Legislatures.

Lux said Bakers potential appointment of a Republican successor is pretty easy to get around given that Democrats have a veto-proof majority and could easily change the law to make it more like other states where if the person who leaves is a Democrat than they have to pick a Democrat.

Debra OMalley, spokeswoman for the Massachusetts secretary of the commonwealth, said, Theres no provision for the state legislature to veto the choice by the governor.

But OMalley noted that the Massachusetts state legislature could change the law between now and next year and override a veto.

This is the law as it stands now. That law could change and in fact that law has changed multiple times in the last 20 years, she added.

Charles Chamberlain, chairman of liberal advocacy group Democracy for America, said it would be worth changing Massachusetts election law to get Warren on the ticket.

If she were to join the ticket as the vice presidential candidate, I think it would make progressives across this country very excited for this ticket, he said. It would really make a difference in motivating, energizing and engaging progressive voters.

Chamberlain said even if Baker picks a temporary Republican successor, that could pave the way for Rep. Ayanna PressleyAyanna PressleyWarren's VP bid faces obstacle: Her state's Republican governor Democrats blast CDC report on minorities and COVID-19 Overnight Defense: Pentagon memo warns pandemic could go until summer 2021 | Watchdog finds Taliban violence is high despite US deal | Progressive Dems demand defense cuts MORE (D-Mass.) to make the leap from House to Senate.

Were talking about a very short period of time until the next special election, when we could be looking at Ayanna Pressley as the next senator from Massachusetts.

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Warren's VP bid faces obstacle: Her state's Republican governor | TheHill - The Hill

Given the potential delays and scope that removing Huawei 5G kit from existing 5G networks and sourcing replacement kit for hardware already on order the need various UK telecoms operators need to have an urgent statement on the record from the Government over exactly what is to happen with Huawei hardware in the UK.

As things stand today all the press coverage is based on a quote from a spokesperson originally quoted in The Guardian and Telegraph.

In January 2020 it was announced that use of Huawei 5G kit would be capped at 35% of the core network, but it appears now after protracted pressure from the United States of America that this is set to change to 0% of the core and its unclear what the fate of Huawei kit elsewhere will be e.g. 3G/4G/5G antenna, existing VDSL2 cabinets, Huawei FTTP ONT and Huawei headends in exchanges.

If there is a proven hack/leak of sensitive data from Huawei kit then one presumes that evidence would be produced or if of a sensitive nature statements would be made pointing this out and trusted parties allowed to verify this. For now though it looks more like a mixture of geo politics and fears of the unknown are driving policy.

In the 5G world the options if you ignore Huawei are basically Nokia and Ericsson and if the issue is really China spying how confident can anyone be that the supply chains of competitors are not compromised. If China is using its electronic exports to run spy networks it is unlikely they will be tied to a specific company and extra code or hardware to syphon off data will be in many places.

Huawei has been under scrutiny for some time and one presumes the same scrutiny will apply to hardware and software from other suppliers.

The 5G networks if they were fully standalone are at a size where removal of Huawei is difficult but reasonably possible. The problem is that improvements in the fifth generation of mobile network also includes core network upgrades that will already have other mobile traffic using them.

Removing Huawei from the 1,200 or so handover exchanges i.e. replacing Huawei head ends with Nokia is again possible and as the amount of FTTP increases there are exchanges with Nokia headends already so for some there is potential for this to be a reconfiguration and rewiring exercise and ensuring wholesalers have connectivity on the Nokia headend.

The issue around Huawei VDSL2 cabinets of which there is at least 66,000 on street corners around the UK is harder as the least downtime solution would be to stand and commision a new cabinet and then move the copper connections down. As this would need a new spot on the pavement that will be difficult in some areas and this a turn and lift off old cabinet and replace with a new one might be cheaper but could mean a days downtime. The more likely solution is to commit to no new Huawei VDSL2 cabinets, which given the small numbers being added each week is not difficult and then once the Salisbury WLR switch off and porting of an entire exchange to FTTP has worked successfuly other areas will follow and the Huawei cabinets removed. How long to remove all the Huawei cabinets is a big guess but given that the FTTP build has to be complete looking at around 2030.

Who will pay for all the work and new kit is unknown, hence the why we need official statements and budget support announced so if the nuclear option is what is desired that firms can start planning immediately.

Of course even if the UK was Huawei free as soon as anyone went to any country that had Huawei kit the worries would return.

A final reason why on the record statements are needed is that in an information vacuum conspiracy theories thrive and a banning of Huawei 5G kit is no doubt going to be latched onto by some either for their own gain be that financial or just giggles.

Originally posted here:
Huawei to be shown exit door from 5G and possibly telecoms networks allegedly - thinkbroadband.com

Give readers an assignment that includes pancake mix, and you get crpes galore (four submissions)! And bread, waffles, biscuits and crackers, too.

Earlier this month, for Round 2 of the Chopped-inspired Seattle Times Pantry Kitchen Challenge, we asked you to make a dish with pancake mix, olives, cheese and herbs.

The olives, it seems, stumped or at least inconvenienced some, while others agonized over what to do with the box of pancake mix thats been gathering dust in the back of the cabinet since, oh, 2010?

But, once again, our readers flexed their culinary muscles and got to work. It was heartening to see several repeat challengers! And, because the ingredients were released around Mothers Day, many of you took the opportunity to do this with mom! Here are the innovative, largely Mediterranean-influenced results.

Since I once again have to pick winners without tasting the dishes, I looked mostly for creative recipes that generously incorporated all four ingredients. Based on that criteria, I declare Jeff Abrams pancakefettuccine the winner of this round! Yes, folks, that enterprising chef made pasta out of pancake mix!Runners-up: Darci Rogojins savory Greek cupcakes andPaul ShapirosTimpano di quarentena.Both epitomized the idea of turning a breakfast ingredient into dinner, and looked pretty darn impressive!

Here are the top 12 submissions. Thanks for playing, and see below for details on Round 3.

Next up:Round 3 of The Seattle Times Pantry Kitchen Challenge

New ingredients:Ground meat (or ground meat substitute), canned fruit, lettuce, soy sauce.

Rules: You have to use all four ingredients; you can, however, add as many additional ingredients as you choose.

Deadline: Create a dish, tell us how successful you were and email photos and adescription of your dish to food editor Stefanie Loh (sloh@seattletimes.com) by June 5. Well pick several of the most interesting submissions we receive to run in a future edition of The Mix.

*For brevity, we decided not run full recipes, but if you would like recipes to any of these dishes submitted by readers, email me and I will send them to you.

Upon hearing pancake mix was an ingredient in this weeks challenge, I immediately thought of pasta. When you have two daughters who live for noodles, thats not surprising. Weve spent years experimenting with pasta-making; from pathetic attempts using hand-crank machines, to better results with finer grades of flour, to egg-filled ravioli.Pasta is nothing more than flour and egg, right? Same as pancake mix, with a few little additions: things like buttermilk, sugar and baking powder.

With the ignorance of naivet, I set forth to make noodles from pancakes.The pasta is definitely on the firm side with a good chew, reminiscent of shaved Chinese noodles. Rolling them as thin as possible helps, but the whole grain pancake flour just isnt that elastic. Im not sure this would have worked without the addition (of) semolina flour. I was worried that the noodles would be sweet, since sugar is the fourth ingredient on the pancake mix bag. Surprisingly, they turned out to have a nice nutty taste, not sweet at all.The green olives are a bit salty, but their brininess really helped cut some of the pastas heaviness.We enjoyed the dish, surprised that pancake mix would make such delicious fettuccine pasta.Since the dish is fairly healthy, vegetarian, with only a tiny bit of fat, plus some protein from the buttermilk and cheese, the Parmesan crackers shape symbolizes heart-friendly.

Jeff Abrams

This challenge had me a little scared but I had so much fun again! After thinking about the ingredient list for a few days, this recipe idea came to me while I was making cupcakes for Mothers Day. In reading the pancake mix box, I saw that I could make drop biscuits so I had the idea of adding a topping and incorporating all the other basket ingredients. I went with a Greek theme because my husband and I did a cruise last October where we visited Santorini, Athens, Rhodes and Corfu, so Greek food has been on my mind since then.

Darci Rogojin

This was tons of fun. I recently made a timpano for a prequarantine party for a good friend. So I thought, Will this work with the Seattle Times ingredients? Voil, or should I say, presto Timpano di Quarantena. I wasnt sure when I took it out of the oven that it would hold up, but it did. I used the olives as a replacement for the traditional salami. We are having it for dinner tonight, and I have to admit, the pancake crust was (and I hate to admit it) better than pasta. Buon appetito.

Paul Shapiro

Really, pancake mix? The ancient box stuck in the back of the pantry, not having seen the light of day since the last annual camping trip? A little homework and perusing the ingredients (looks suspiciously like buttermilk biscuits) pointed to naan bread as a possibility. One thing lead to another and we decided on Olive These Lamb Meatballs on garlic naan. Had we made it before? Nope. Did we follow a recipe? Sort of. Was it good? Oh yeah!While this might look complicated, in reality, it came together rather quickly. The olive chimichurri was a nice foil to the slight sweetness from the pancake mix. Lamb meatballs are rich and the tzatziki counters the richness with acidity from the yogurt and lemon juice.

Steve Venard and Cathy Martin

Last December, I spent a week in northern Italy visiting Christmas markets. At one night market, we bought a slice of bread that was a warm, gooey slice of heaven on a cold, clear night. It reminded me of a savory version of the age-old monkey bread but filled with olives and cheese. Your challenge of pancake mix, olives, cheese and herbs motivated me to try my version.

Joan Segna

For this Round 2 of pancake mix, olives, herbs and cheese, we made what my mom is calling chicken olivetti parmigiana.I (42) am in Seattle, and she (65) is in upstate New York. She is a very skilled home cook, and I am not, so during this time of quarantine, she has been coaching me over the phone, which is what happened for this dish.For the challenge, I used Trader Joes buttermilk pancake mix as the base for the dry dip. This included some Parmesan cheese and herbs. After quickly pan-frying the coated chicken (and putting it in the oven for a few minutes), I drizzled a simple butter sauce with sliced mixed olives and olive juice on top. The chicken had a nice light crunch on the outside, was moist and tender on the inside, and the olive sauce added a nice briny, salty topping.

Tracy Timmons-Gray and Patti (Tracys mom)

My husband is a pancake fiend, so we have the mix on hand (though his preferred brand is MIA on the shelves these days). He is the sweet and I am the savory of our pair, so olives and cheese sounded fabulous to me. My herb garden is so robust right now that was a simple go-to. How to get Betty Crocker into the savory realm: Adding chopped chives and an egg gave some taste to an otherwise bland, classic mix, and before we flipped the bottom cake we strewed on a lot of finely grated Parmesan. The cheese made for a crunchy layer with a nice bite and color and the bottom cake didnt get soggy from liquid in the tapenade. Not to be eaten warm. We chopped all the miscellaneous olives on hand for tapenade and jazzed it up with pimentos and a good bit of freshly picked chopped thyme, then spread this on the big cake. The filigree top was added last and that batter was a bit thinner with added milk. Because it is hard when doing a speedy, masked duty run to the grocery to resist a bit of decadence to offset the monotony of these days, we had crme frache on hand for a fancy dollop to top it off. The crme added just the right schmear. With cheap, chilled white wine, it was pretty darn good, though a lot more herbs in the batter would alter some of the pancake taste.

Pandora Touart and Tom Whitaker

I made a potato galette with an olive and caper tapenade and sourdough and pancake mix crust. It turned out better than I expected! Ive been looking for creative ways to use up my sourdough discard, and pie crust is something I am really good at making. The tapenade is made up of Castelvetrano olives, capers, thyme, rosemary, garlic, Parmesan cheese, lemon juice and olive oil. Really briny and delicious on its own, but even better with the potatoes because it evens out the strong flavors. Did I measure anything? Not really. Would I attempt again? Definitely!Thank you for this challenge! I had fun with this, and cant wait for the next round!

Sheree Diep

My mom and I share the same zodiac sign and we read our horoscope in The Seattle Times on a daily basis for the fun of it. When I read todays, I felt compelled to literally stick to tested methods, techniques and recipes as mom was already getting ready to make our family-favorite dish, red lentil cake. I challenged her to use pancake mix as a substitute for baking soda and baking powder. She is gluten-intolerant, so we used gluten-free pancake mix. Olives and cheese are already two breakfast items we love as part of our old country Mediterranean breakfast, as well as plain yogurt, which we consume an insane amount of. Our new version definitely beat our expectations and tasted better than our original recipe. Red lentil cake is seriously perfect any time of the day.

Bahar Oguzertem and Yasemin Alptekin (Bahars mom)

I had a great time putting together this olive tapenade with rosemary pancake crackers! This was more of a starter than a meal in itself, but my wife and I thought it turned out well! Perhaps the tapenade would shine better with a more neutral cracker, but the combination of the salty tapenade and the sweet, toasty crackers highlighted a range of flavors that would transform throughout the bite. The rosemary pancake crackers were simply a much drier ratio of pancake mix to water, the addition of some oil and chopped, dried rosemary. Baking it, after mixing and rolling thin, for a total of 6 minutes and letting it sit in the turned-off oven for an hour allowed it to dry up into a crisp and crunchy cracker! The tapenade included green, black and Kalamata olives, garlic, shallot, oregano, thyme and to round out the last of the four challenge required ingredients cheese! More specifically, cotija cheese left over from Cinco de Mayo. Traditional tapenade usually includes anchovy, but the salty cotija acted as a fine substitute. This was a really fun challenge! It stretched my creative muscles and I cant wait to see what lies in store for Round 3!

Jared Cook

I used the pancake mix to make crpes by adding egg and more water. Then added chopped Kalamata olives, orange zest, basil and honey to goat cheese. This was all wrapped up in the crpes to make a really good hors doeuvre.Ill be making it for my next dinner party!

Lynn Fischer

I used Seattles own Krusteaz pancake mix with a half-cup of water to 1 cup of the mix. I added sliced and drained black olives, grated white cheddar (about a half-cup), minced basil and parsley from my garden and 1 teaspoon of baking powder.Baked at 350 degrees F for about 15 minutes, delicious.

Wendy Reilly

Continue reading here:
Make a dish with pancake mix, olives, cheese, herbs? Seattle Times readers show off in Round 2 of the Pantry Kitchen Challenge. - Seattle Times

Asymptomatic testing for the novel coronaviruswill begin immediately for anyAlbertan who wants it, Alberta's Chief Medical Officer of HealthDr. Deena Hinshawannounced Friday.

The move is inpreparation for Stage 2 of the province'srelaunch, expected totake place in mid June,Hinshaw said at a news conference.

"Now is an opportune time to expand testing to get a full understanding of the presence of COVID-19 in our population," Hinshaw said.

"Expanded testing will ...help us understand where there might be undetected positive cases and therefore prevent further spread of the virus.

Albertanswithout symptoms of illness can arrange testing by going online,completinganassessment tool and booking anappointment,she said.

Anyonecan be tested as often as they like, Hinshaw said.

"There's no restriction on the frequency with which people can access testing, so that is not something that would limit people's access," she said. "The recommendation would bethat testing should never be used as a replacement for public health measures."

The date of Stage 2 will be discussed next week, Hinshaw said.

"I said before given the low case numbersreally encouraging results from Stage 1 relaunch that we are considering whether or not we can advance the date of Stage 2.

An increase in hospitalizations over the last few days is a concern, Hinshaw said.

"Hospitalizations are a function not only of our total cases but also of who is exposed and is infected with the virus so those who are older, have medical conditions, are at higher risk of having more severe illness and needing hospital care.

"It's something we're watching very closely, but at this point, we haven't seen an uptick in our total number of cases.

"We'll continue to watch both total cases and hospitalizations very closely, but at this point, I don't anticipate what's happened these last few days would change our discussions regarding relaunch."

Alberta reported no new deaths from COVID-19 Friday, and 24 new cases of the respiratory disease.

There are 616 active cases in province, with 55 people in hospital, four of them in intensive care.

The provincial COVID-19 death toll remains at 143, while 6,220 people have recovered.

Hinshawalso announcedthe final part of Stage 1 of the relaunchwill begin Monday in Calgary and Brooks, two cities hit hard by the virus.

"Albertans in these two cities have been patient as we took a measured, phased approach to their relaunch," Hinshaw said.

"I would like to congratulate residents in these cities on the downward trends in their numbers, which has not been seen in most places around the world where relaunch has happened."

The majority of active cases of COVID-19 continue to be located in the Calgary zone. Here's a regional breakdown of cases:

Day camps and summer schools in Calgary and Brooks will be able to open Monday with occupancy limits, whileplaces of worship may resume in-person services for up to 50 people, with precautions to limit the potential spread of infection.

Premier Jason Kenney announced this week the province's state of public health emergency, in place since March 17,will be allowed to lapse on June 15.

Declaring the emergency gave the provincial cabinet many newpowers, includingthe ability to limit the size of public gatherings andrequire people to self-isolate.

"We don't need the declaration of a public health emergency to protect people from COVID," Hinshaw said. "We will still be able to ...respond to COVID-19 and protect Albertans. The emergency really is a measure that allows a significant and rapid response, but if that measure is ended ... we do have other tools we can use."

The next update with Hinshaw is scheduled for Monday, June 1.

Read more from the original source:
Alberta moves to provincewide COVID-19 testing available to all symptoms or not - CBC.ca

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...

The U.S. Senate confirmed U.S. Rep. John Ratcliffe, R-Heath, as director of national intelligence Thursday, elevating him to a cabinet-level position in the Trump administration and creating a vacancy for a congressional seat in Texas.

The confirmation vote was 49-44 and brought a relatively smooth conclusion to a nomination process that started off rocky. President Donald Trump first tapped Ratcliffe for the position, which oversees the nation's 17 intelligence agencies, in July. But his path to becoming director of national intelligence initially hit a snag when the The Washington Post reported that a claim on Ratcliffe's website that he arrested "over 300 illegal immigrants on a single day" as a federal prosecuting attorney was an exaggeration. He also faced questions over whether he overstated his role as a federal prosecutor in a terrorism financing case.

Ratcliffe withdrew from consideration within a week as questions were raised about his credentials and whether he inflated parts of his biography. But Trump nominated him six months later, calling him an "outstanding man of great talent." Ratcliffe has been a vocal ally for Trump, defending the president during impeachment hearings in 2019. He was reportedly considered as a potential replacement for former Attorney General Jeff Sessions.

His nomination has received strong support among Republicans. At Ratcliffe's confirmation hearing, U.S. Sen. John Cornyn, R-Texas, called the nominee a man of character who understood the difference between being a politician and being an appointed official. Retiring U.S. Rep. Will Hurd, R-Helotes, a former CIA undercover agent, also endorsed Ratcliffe, citing his professional experience, "capacity to selflessly lead," and understanding of "threats to our security and way of life."

All of the Democrats on the Senate Intelligence Committee voted against advancing his nomination. But the minority party allowed the chamber to move quickly on a full vote on the nomination this week in order to get a Senate-confirmed nominee into the job in place of controversial acting Director Richard Grenell.

In his confirmation hearing, Ratcliffe expressed a need for the intelligence community to remain apolitical.

I will deliver the unvarnished truth," Ratcliffe said. "It wont be shaded for anyone. What anyone wants the intelligence to reflect wont impact the intelligence I deliver.

Meanwhile, the race to replace Ratcliffe has already begun in his northeast Texas district. Ratcliffe already won the Republican primary for the seat, meaning a group of activists that make up what is called the Congressional District Executive Committee will select his replacement on the November ballot. The committee will meet Aug. 8 to select a nominee.

Gov. Greg Abbott will not call a special election to finish Ratcliffe's term this year, according to an Abbott spokesman, John Wittman.

Ratcliffe's initial election to the seat in 2014 came as a surprise to many and was hailed as a sign of the power of the Tea Party movement. That year, he unseated the late Ralph Hall, R-Rockwall. Hall was a 91-year-old, 17-term congressman.

Abby Livingston and Patrick Svitek contributed reporting.

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John Ratcliffe confirmed as director of national intelligence - The Texas Tribune

It remains unlikely, but hardly unthinkable, that President Donald Trump and Vice President Mike Pence could simultaneously come down with serious cases of COVID-19 especially after two prominent White House aides recently tested positive for the coronavirus. We have already seen one head of government, British Prime Minister Boris Johnson, incapacitated by COVID-19 and sent to an intensive care unit.

Both men are in high-risk groups: Trump is 73 and overweight; Pence is 60. (Johnson, in contrast, is a comparatively youthful 55.) If they were ordinary people, the protocol would be for the two men to place themselves in self-quarantine for two weeks, yet they have not done so.

When Johnson was hospitalized, he deputized his foreign minister to act as prime minister in his absence. Should only the president become ill, then the vice president can take over, following the protocol laid out in the 25th Amendment. But if the vice president becomes incapacitated as well, then we could face a constitutional crisis. It would be triggered by the inadequacies of the Presidential Succession Act passed in 1947 (when there was no vice president, because Harry Truman had succeeded Franklin D. Roosevelt).

Article II of the Constitution grants Congress the right to provide for the Case of Removal, Death, Resignation or Inability, both of the President and Vice President, declaring what Officer shall then act as President and the 1947 act is the current result. Under its rules, the speaker of the House and the president pro tempore of the Senate would be next in the line of succession, followed by the members of the Cabinet, beginning with the secretary of state.

Until 1947, succession had passed through the Cabinet. Congress added the speaker and president pro tem on the grounds that the president should desirably be an elected official, even if not part of the executive branch. This might make sense in theory, but it could be truly terrible in practice. Should both Pence and Trump be unable to serve, Speaker Nancy Pelosi, D.-Calif., would become president under the act handing the White House to a different party without an election. Should she be unable or unwilling to serve, then the office would go to Sen. Charles Grassley, R-Iowa.

Any effort to transfer power from Trump and Pence to Pelosi would surely inspire legal and political challenges, adding to chaos at precisely the moment the nation desperately needed stability.

To be sure, COVID-19 in the White House could precipitate a crisis well before the Succession Act came into play. It is not difficult to imagine that Trump would deny and denounce as fake news any suggestion that he lacks the ability, in the words of Article I, Section 2, of the Constitution, to discharge the Powers and Duties of the presidency. The vice president and Cabinet can, in theory, overrule him and pronounce him unable to serve, invoking the 25th Amendment. But would they? Even if Pence and the Cabinet displayed independence, would Trump simply fire those who betrayed him? He couldnt fire the vice president, but the vice president cannot displace a president on his own; he needs the support of the majority of Cabinet officials and then Congress.

But even if the headstrong president bowed to reality, perhaps as he was about to go on a ventilator, the system would be stretched to the breaking point if Pence faced his own health crisis. If Pence, too, acknowledged his constitutional inability, then the Succession Act would apply and its flaws would become apparent.

The act, first of all, bespeaks a simplistic theory of democratic legitimacy that ignores the prominent role that political parties which have grown far more polarized since 1947 play in the American system. And it raises vexing legal and practical questions. Most lawyers believe that the speaker would have to resign from the House to serve as president, as a result of the Constitutions obscure incompatibility clause, which says that no Person holding any Office under the United States, shall be a Member of either House during his Continuance in Office. Perhaps, then, Pelosi would waive her right of succession (since, after all, her term would probably last only several weeks at most). So then the 86-year-old Grassley could take on the awesome role of president should he be willing to resign from the Senate.

There is also a serious argument, first laid out by Yale Law School professor Akhil Reed Amar and his brother, Vikram Amar, now dean of the University of Illinois College of Law, in a 1995 essay in the Stanford Law Review, that the Succession Act is unconstitutional. Article II specifically says that Congress in setting rules of succession must select an officer as a replacement for the president and vice president. Members of Congress, the argument goes, are not officers, because they are elected officials and not presidential appointees. (Another legal argument holds that the incompatibility clause does not apply if a member of Congress were to serve as president or vice president, because officers refers to people appointed by the president, not to the chief executive position itself. Under that interpretation, Pelosi could retain her legislative office, if the act were upheld as constitutional.)

To put it mildly, it is hard to imagine these questions being litigated in real time should Republicans try to prevent Pelosi from taking office, or should she try to serve as president and speaker simultaneously. This month, Justice Brett M. Kavanaugh evoked the possibility of chaos in a Supreme Court argument about unfaithful electors members of the electoral college who opt for candidates besides the ones they pledged to support. The problem of unfaithful electors is trivial compared with the true chaos possible under the Succession Act.

Constitutionality aside, the Succession Act makes little sense as policy: No one seriously believes that the worthies who serve as speaker of the House and president pro tem of the Senate do so because of a belief by the House or Senate that they have the skill set needed to serve as president. Indeed, Grassley occupies his office exclusively because he is the senior member of the majority.

Just as the United States turns out to have been woefully unprepared to confront the coronavirus, so are we unprepared to confront simultaneous presidential and vice-presidential disability. Returning to the pre-1947 rules, under which the secretary of state would follow the vice president in the line of succession, would make far more sense. The Constitution authorizes is it too much to suggest that it even places a duty on? Congress to address the possibility that the president and vice president could both become incapacitated. It should face up to its responsibility, before the grim scenario becomes reality.

Sanford V. Levinson is a professor of law and government at the University of Texas at Austin. He wrote this article for the Washington Post.

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OPINION EXCHANGE | If Trump and Pence both get very sick, it's unclear who would be president - Minneapolis Star Tribune