text:
Gilead Pours Cold Water on Good News About Coronavirus Drug Trial
Posted by John Titor | Apr 17, 2020 |
After a one-two punch of disappointing news for Gilead out of China, where as we reported yesterday a trial of remdesivir in Covid-19 patients with mild or moderate symptoms was suspended as no eligible patients could be recruited according to an update on ClinicalTrials.gov, which in turn followed a trial of the drug in severely ill Covid-19 patients which was also terminated for similar reasons, it felt like one or more hedge funds would seek to manipulate public sentiment – and the market – and get out of an underwater position.
They did just that at 430pm today when the low profile Statnews published a report by Adam Feuerstein, best known for doing the bidding of one or more hedge fund clients, according to which a "Chicago hospital treating severe Covid-19 patients with Gilead Sciences' antiviral medicine remdesivir in a closely watched clinical trial is seeing rapid recoveries in fever and respiratory symptoms, with nearly all patients discharged in less than a week, STAT has learned."
"Learned" from whom? A hedge fund or two who were long GILD and needed a fake news catalyst to get out? Because last time we checked, double blind, secret drug trials are, er… secret.
The report went on: "patients using Gilead's remdesivir were "seeing rapid recoveries in fever and respiratory symptoms", which is odd considering the two previous incomplete/failed trials in China. According to the report, "almost all patients were discharged in under than a week, and only two patients died."
Which actually is very bad news because as the FT reported later – this time using facts not sources – "none of the patients on the Chicago trial had been on invasive ventilation", so is it possible that the drug killed them? Maybe Feuerstein can ask his hedge fund source.
In any case, at the time the data hit Bloomberg around 4:21pm, it barely registered among the institutional audience, or even the algos, as most serious players were well aware of Feuerstein's "tactical" reputation.
It wasn't until the news was reported by CNBC about 30 minutes later – and hit the retail investing audience – that GILD stock soared 16 and the entire market took off as if someone had suddenly discovered not only a cure but also a vaccine for coronavirus.
Alas, neither happened, and as it has done on numerous occasions previously, about three hours after the news broke conveniently providing one or more hedge funds ample opportunity to offload their position in the thin after hours market, Gilead issued a statement which suggested that STAT was pumping the stock based on nothing more than "anecdotal reports" to wit:
"We understand the urgent need for a Covid-19 treatment and the resulting interest in data on our investigational antiviral drug remdesivir. The totality of the data need to be analysed in order to draw any conclusions from the trial."
"Anecdotal reports, while encouraging, do not provide the statistical power necessary to determine the safety and efficacy profile of remdesivir as a treatment for Covid-19. We expect the data from our Phase 3 study in patients with severe Covid-19 infection to be available at the end of this month, and additional data from other studies to become available in May."
And so, one more attempt to ramp the market using fake news of an imminent drug has failed, with some observing that a Phase 1 drug trial is now the functional equivalent of a Phase 1 trade deal – both meaningless on their own, but with the power to send stocks surging when used tactically by unknown players.
Experts had high hopes for remdesivir because it had shown it was effective in stopping the Ebola virus replicating. Ebola shares a similar mechanism of replication with Sars-CoV-2, the virus that causes the Covid-19 disease. However, there has been no definitive statistic evidence yet that that is the case.
As a result, the drug has not been approved to treat any disease and scientists are waiting for the results of large trials with control arms for proof that it works and that the benefits outweigh any risks of side-effects. Umer Raffat, a biotech analyst at Evercore, said that he was "cautiously optimistic" about remdesivir but warned it was not a "silver bullet".
He said it was important to note that none of the patients on the Chicago trial had been on invasive ventilation, which tends to be an indication of a more serious condition and worse outcomes. In short, the observed response could have been nothing more than a placebo effect.
And while stocks soared to new one month highs on the initial overhyped news of a trial that has not yet even concluded amid the latest powerful short squeeze, the rally has since reversed even if the drop has a long way to go before it fills the gap. Meanwhile, stocks are now the most overvalued ever, ever on the basis of forward multiples.
In short, we just observed another marketwide pump and dump, in which whales used gullible, CNBC-watching Joe Sixpack retail investors for "distribution"
Republished from ZeroHedge.com with permission
John Titor

redpajama_set_name: RedPajamaCommonCrawl


The Office of the Governor promotes disability inclusion through the Governor's Committee on People with Disabilities.
National Disability Employment Awareness Month (NDEAM) Poster Art competition. Entries must be submitted by May 31, 2017.
Above are examples of previous poster contest winners. Use the link below to learn more about the artists and their work.
The winning artist gets statewide recognition when the committee releases free copies of the winner's art on posters to businesses across Texas.
"The winning artwork is incorporated into the Texas HireAbility Campaign #TXHireAbility," according to the press release.
In 2016, the committee distributed 2,500 posters.
Submissions from Texas artists with disabilities can be sent to the committee via email at GCPD@gov.texas.gov with a photo attachment of the original work in a high resolution digital format, either JPEG or PDF.
They also accept color photocopies, or images on a CD sent by postal mail. The original artwork does not need to be submitted unless it wins the competition. It is free to enter.
All entries must be accompanied by a signed Entry Form, which is available on our website along with the Submission Guidelines.
Entries must be received by email or postmarked by May 31, 2017. The winner will be announced by June 21.
Besides having their art on a poster that is distributed across the state, the original art and the poster will be placed on display in the Office of the Governor's Committee on People with Disabilities, as well as at other exhibits.
See previous winners at this link.
The winning artist may opt to be a featured guest at the annual Lex Frieden Employment Awards ceremony this October. Sign Shares, Inc./International won a Lex Frieden award for its support and inclusion of employees with disabilities.
Join Sign Shares in ensuring that your office provides access an inclusion in the workplace. If you have an employee who needs sign language or foreign language services, request language services with Sign Shares.
Since a popular talk at Stanford University, a scholar who is Deaf has continued her quest to understand the beauty of both sign language and English.
Rachel Kolb presents at TEDx about her experiences with English and sign language.
Kolb was born profoundly deaf and knows sign language.
Kolb tells the story of a presentation she gave to her middle school history class.
This lack of awareness causes some in the d/Deaf community to shy away from speech, but not Kolb.
She has challenges though, she said, and one of those is social communication with hearing people when she relies solely upon reading lips.
Kolb shares about what her speech therapist told her mother when she was 18 months old–to not limit her.
"I communicate fine face-to-face, but walking into those kinds of group conversations is like watching a world championship ping pong match with ten different people and half a dozen balls," Kolb said.
In another article, Rachel Kolb's mother, Irene Kolb, shares about learning what to do about her daughter Rachel's hearing loss. Irene went to the library and read about hearing loss and communication.
"I learned that the biggest window of opportunity for language acquisition is from birth to three years. We started using signs that same day and within a few months, Rachel was communicating to us with baby signs," Irene Kolb said.
Kolb's father, Bill Kolb, shared a story about how he came to understand deafness through a New Mexico state-sponsored program.
According the the article, her parents learned sign language over lunch where they worked and took continuing education sign language classes. Their daughter Rachel studied at Deaf, mainstream, and private schools—an environment that may have helped her develop an appreciation of diverse communication.
"Having a family that signed and that worked to provide language access for me gave me a sense of confidence in myself, even when things got challenging," Kolb said.
Stanford News reported that Rachel Kolb had a unique perspective on communication.
While she doesn't have a choice over everything, Kolb says, she does have a choice over how to use her abilities, which include sign language and English.
"The general advantages of sign are numerous: not only talking through overwhelming noise, but chatting to friends from various distances, or through barriers like doors or windows. Sign, too, possesses a vibrant visual-spatial orientation and a robust directness of expression that spoken languages lack," she said.
She said people who are hearing note her ability to visually navigate a loud environment where hearing people have difficulty too. They've commented that it would be preferable to use sign and she encourages them to learn.
Communication is unique to the person and situation, to their education and experiences, but it's valuable to embrace flexibility in communication with others, regardless of ability.
Rachel Kolb demonstrates what it's like to read lips in this video that gives you an opportunity to test your lipreading and deafness skills.
Singer, songwriter, and musician Zoë Nutt recently released a song, "Like You," telling the story of her progressive hearing loss.
100 percent of the artist's tip proceeds from downloads of "Like You" at Noistrade will go to The Hearing Loss Association of America.
You can order Nutt's album on iTune's or in physical CD here.
Download "Like You" for free here.
Two groups have created a variety of useful materials about communication disorders that are free to use and distribute.
To raise awareness about communication disorders, the National Institute on Deafness and Other Communication Disorders and the American Speech-Language-Hearing Association observe Better Hearing and Speech Month each May.
This month, they are holding a social media contest and providing an inforgraphic and a quiz about hearing loss, as well as other communication disorder information in press releases, information sheets, posters, and more.
According to the institute's website, 48 million Americans have a form of communication disorder, while 37.5 million of us have hearing loss–that's 15 percent of Americans.
Want to use the infographic above? The institute has created a version you can upload to your website or blog here.
Currently, the institute supports research for promoting accessible health care and urges people who think they have hearing loss to have their hearing tested.
Since only 25 percent of Americans who could benefit from hearing aids have used them, there are potentially people who might want them who haven't had access to them, or who may need them for safety or work-related issues.
On their website, you may take a quiz in the right-hand column to determine if you should have your hearing tested.
The American Speech-Language-Hearing Association is promoting a social media contest to raise awareness. The association will award points for sharing information on social media and issue prizes for those earning the most points. Prizes will include Amazon gift cards and association promotional materials.
Learn what people with communication disorders, audiologists, speech-language pathologists are doing to raise awareness on this interactive, international map.
The association has also provided many press release materials and patient information handouts about hearing loss–in English and Spanish.
Sign Shares, Inc./International educates society about awareness issues concerning communication disorders through this blog and also on our website, including answering questions people ask about using sign language interpreters and information about how to empower communities by providing communication access.
Request sign language services from Sign Shares here.
Are you prepared for when an emergency strikes?
The Texas State Independent Living Council will host a webinar/virtual conversation, "Conversations on Independent Living: Emergency Preparedness," on May 5, from 2 p.m.-3 p.m. (CT).
The guest speakers will be Ron Lucey, Executive Director of the Governor's Committee on People with Disabilities, and Danielle Hesse with the Texas Disability Task Force on Emergency Management of the Texas Department of Public Safety.
increasing emergency preparedness in the disability community.
This discussion is open to all members of the public.
Accommodations: If you need accommodations to join the meeting, contact the Texas SILC by Wednesday, April 26, 2017.
For more information, contact Ashley Fry at 512-371-7353 or ashley@txsilc.org.


text:
Comparing José Mourinho's Record After 20 Games at Each Club He's Managed
?José Mourinho's 20th game in charge of Tottenham will be the home clash with Aston Villa in the Premier League later this month.
So far he's collected ten wins and four draws as Spurs boss, with the Lilywhites suffering five defeats during his tenure.
Having been 12th in the table when Mourinho took charge, Tottenham now find themselves fifth and just four points behind Chelsea, who occupy the final Champions League qualification spot.
While we're all still trying to figure out the exact style of play the Portuguese manager is looking to implement in north London, results have certainly picked up, though it would have been difficult to makes things worse considering the disastrous end to Mauricio Pochettino's reign.
To get a better sense of where ?Spurs are heading, let's take a look at Mourinho's record after 20 matches with each major club he's previously managed.
Record After 20 Games: 15 Wins, 2 Losses, 3 Draws
Mourinho's first experience at a top level side came with Porto back in 2002. Suffice to say, he had an immediate impact at Estádio do Dragão.
The Blue and Whites had finished third in the Primeira Liga the year before he arrived, trailing champions Sporting CP by seven points. They went on to win the league by 11 after the Special One joined the party.
Porto's charge to glory saw them remain undefeated in domestic competition until February, overcoming second-place Benfica and fourth-place Vitória Guimarães in a blistering beginning to the Mourinho era.
During their first 20 games under his tutelage, the Portuguese club also secured a spot in the UEFA Cup last 16.
Record After 20 Games: 16 Wins, 1 Loss, 3 Draws
After guiding Porto to the 2003/04 ?Champions League title, Mourinho was lured to Stamford Bridge where he became an instant favourite among supporters and players.
He inherited an already strong squad, but Mourinho knew he needed to make additions if they were to reach the next level. The men he brought in transformed ?Chelsea into an unstoppable machine.
Didier Drogba, Arjen Robben and Petr ?ech headlined the list of new recruits, while Mourinho also took Ricardo Carvalho and Paulo Ferreira with him from Porto. It was the Blues' intelligent business in the summer transfer window that launched them into the stratosphere.
Only Manchester City could overcome them in his opening 20 fixtures, with Manchester United going down 1-0 at the Bridge in the Special One's very first game.
If Tottenham are serious about sealing their place among Europe's elite, they'll need to provide some funds for the boss.
Mourinho's start at ?Inter bares more similarity to his early days in north London, I Nerazzurri far from the finished article during the first few months of his stewardship.
Of course, José would guide them to a first European crown in 45 years in his second campaign, with 2008/09 laying the foundations for their success the following season.
Inter's only loss in Serie A during Mourinho's opening 20 games was a 1-0 defeat at rivals Milan. However, they recovered to end up top of the pile, ten points ahead of runners-up Juventus, who they overcame 1-0 in a seismic clash at San Siro in November.
It's worth noting this Nerazzurri squad contained some superstars, including Zlatan Ibrahimovi? and Luis Figo, while more were added ahead of 2009/10.
We have further evidence that it pays dividends to fully back Mourinho in the transfer market. Spurs would do well to show faith in his abilities, whatever he demands. They brought him in for a reason.
It had all been going so well for José in the Spanish capital, and then a brutal Clásico thrashing occurred.
In his 20th match as ?Real Madrid coach, Mourinho watched his recruits suffer a harrowing 5-0 hammering away to ?Barcelona. Los Blancos were woeful at Camp Nou, having been superb throughout the early months of the season.
There were 6-1 victories, a 4-0 win over Ajax, and a 5-1 destruction of Athletic Club, yet the Copa del Rey was the only piece of silverware added to the cabinet in 2010/11. One year on, however, things had clicked into place, and Real clinched their first ?La Liga trophy since 2008.
There's definitely a trend starting to emerge. The first season tends to be used as a stepping stone for bigger things in the second, and that should give Lilywhites fans a boost heading into 2020/21.
Chelsea: Volume 2
Mourinho's second adventure in the Chelsea hot-seat also reflects his journeys with Inter, Porto and Madrid. There was plenty of promise at the beginning with reasonable room for improvement before a superb second season.
The signings of Diego Costa and Cesc Fàbregas proved decisive, the Blues going on to lift the Premier League title as the Special One once again produced a remarkable follow-up campaign.
In the first 20 games, Mourinho secured a victory against eventual winners Manchester City, while United and Spurs were both held to draws.
José's record versus the big boys isn't quite so favourable as Tottenham manager, with Liverpool, United, and Chelsea all defeating his side, though more recently there was the 2-0 win against Manchester City.
Mourinho enjoyed a strong start after taking the reins at Old Trafford, beating ?Leicester in the Community Shield in his debut game.
Things swiftly went a bit stale, however. Their title challenge fell by the wayside by match 20 of his tenure, with successive defeats to City, Europa League opponents Feyenoord and ?Watford? leaving fans concerned. A 4-0 loss to Chelsea did little to ease the tension.
His early record with the Red Devils is reasonably similar to his stint at Spurs, though there were plenty of differing variables at play both on and off the field at Old Trafford compared to Tottenham Hotspur Stadium.
For more from Ed Alexander, follow him on Twitter
Previous Previous post: Football Wildcards: Players That Never Did What They Were Told (On and Off The Pitch)
Next Next post: Man Utd Put Off by Lautaro Martinez Release Clause as Striker Favours Barcelona Move


text:
HomeComputational neuroscience
Gottingen talk
July 11, 2019 July 11, 2019 Carson Chow Computational neuroscience, Conferences, Neuroscience, Talks
Here are my slides for the talk I gave today at The 4th workshop on Advanced Methods in Theoretical Neuroscience, Structure and disorder: From random connections to functional circuits, July 10-12 2019, Göttingen, Germany.
Confusion about consciousness
May 23, 2016 Carson Chow Computational neuroscience, Neuroscience, Philosophy
I have read two essays in the past month on the brain and consciousness and I think both point to examples of why consciousness per se and the "problem of consciousness" are both so confusing and hard to understand. The first article is by philosopher Galen Strawson in The Stone series of the New York Times. Strawson takes issue with the supposed conventional wisdom that consciousness is extremely mysterious and cannot be easily reconciled with materialism. He argues that the problem isn't about consciousness, which is certainly real, but rather matter, for which we have no "true" understanding. We know what consciousness is since that is all we experience but physics can only explain how matter behaves. We have no grasp whatsoever of the essence of matter. Hence, it is not clear that consciousness is at odds with matter since we don't understand matter.
I think Strawson's argument is mostly sound but he misses on the crucial open question of consciousness. It is true that we don't have an understanding of the true essence of matter and we probably never will but that is not why consciousness is mysterious. The problem is that we do now know whether the rules that govern matter, be they classical mechanics, quantum mechanics, statistical mechanics, or general relativity, could give rise to a subjective conscious experience. Our understanding of the world is good enough for us to build bridges, cars, computers and launch a spacecraft 4 billion kilometers to Pluto, take photos, and send them back. We can predict the weather with great accuracy for up to a week. We can treat infectious diseases and repair the heart. We can breed super chickens and grow copious amounts of corn. However, we have no idea how these rules can explain consciousness and more importantly we do not know whether these rules are sufficient to understand consciousness or whether we need a different set of rules or reality or whatever. One of the biggest lessons of the twentieth century is that knowing the rules does not mean you can predict the outcome of the rules. Not even taking into the computability and decidability results of Turing and Gödel, it is still not clear how to go from the microscopic dynamics of molecules to the Navier-Stokes equation for macroscopic fluid flow and how to get from Navier-Stokes to the turbulent flow of a river. Likewise, it is hard to understand how the liver works, much less the brain, starting from molecules or even cells. Thus, it is possible that consciousness is an emergent phenomenon of the rules that we already know, like wetness or a hurricane. We simply do not know and are not even close to knowing. This is the hard problem of consciousness.
The second article is by psychologist Robert Epstein in the online magazine Aeon. In this article, Epstein rails against the use of computers and information processing as a metaphor for how the brain works. He argues that this type of restricted thinking is why we can't seem to make any progress understanding the brain or consciousness. Unfortunately, Epstein seems to completely misunderstand what computers are and what information processing means.
Firstly, a computation does not necessarily imply a symbolic processing machine like a von Neumann computer with a central processor, memory, inputs and outputs. A computation in the Turing sense is simply about finding or constructing a desired function from one countable set to another. Now, the brain certainly performs computations; any time we identify an object in an image or have a conversation, the brain is performing a computation. You can couch it in whatever language you like but it is a computation. Additionally, the whole point of a universal computer is that it can perform any computation. Computations are not tied to implementations. I can always simulate whatever (computable) system you want on a computer. Neural networks and deep learning are not symbolic computations per se but they can be implemented on a von Neumann computer. We may not know what the brain is doing but it certainly involves computation of some sort. Any thing that can sense the environment and react is making a computation. Bacteria can compute. Molecules compute. However, that is not to say that everything a brain does can be encapsulated by Turing universal computation. For example, Penrose believes that the brain is not computable although as I argued in a previous post, his argument is not very convincing. It is possible that consciousness is beyond the realm of computation and thus would entail very different physics. However, we have yet to find an example of a real physical phenomenon that is not computable.
Secondly, the brain processes information by definition. Information in both the Shannon and Fisher senses is a measure of uncertainty reduction. For example, in order to meet someone for coffee you need at least two pieces of information, where and when. Before you received that information your uncertainty was huge since there were so many possible places and times the meeting could take place. After receiving the information your uncertainty was eliminated. Just knowing it will be on Thursday is already a big decrease in uncertainty and an increase in information. Much of the brain's job at least for cognition is about uncertainly reduction. When you are searching for your friend in the crowded cafe, you are eliminating possibilities and reducing uncertainty. The big mistake that Epstein makes is conflating an example with the phenomenon. Your brain does not need to function like your smartphone to perform computations or information processing. Computation and information theory are two of the most important mathematical tools we have for analyzing cognition.
New paper in PLoS Comp Bio
May 9, 2016 May 9, 2016 Carson Chow Computational neuroscience, Neuroscience, Papers
Shashaank Vattikuti , Phyllis Thangaraj, Hua W. Xie, Stephen J. Gotts, Alex Martin, Carson C. Chow. Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory. PLoS Computational Biology (2016).
It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.
When the brain is presented with an ambiguous stimulus like the Necker cube or what is known as the quartet illusion, the perception will alternate or rival between the possible interpretations. There are neurons in the brain whose activity is correlated with the perception and not the stimulus. Hence, perceptual rivalry provides a unique probe of cortical function and could possibly serve as a diagnostic tool for cognitive disorders such as autism. A mathematical model based on the known biology of the brain has been developed to account for perceptual rivalry when the stimulus is static. The basic model also accounts for other neural responses to stimuli that do not elicit rivalry. However, these models cannot explain illusions where the stimulus is intermittently switched on and off and the same perception returns after an off period because there is no built-in mechanism to hold the memory. Here, we show that the inclusion of experimentally observed low-level background neural activity is sufficient to explain rivalry for static inputs, and rivalry for intermittent inputs. We validate the model with new experiments.
This paper is the latest of a continuing series of papers outlining how a canonical cortical circuit of excitatory and inhibitory cells can explain psychophysical and electrophysiological data of perceptual and cortical dynamics under a wide range of stimuli and conditions. I've summarized some of the work before (e.g. see here). In this particular paper, we show how the same circuit previously shown to explain winner-take-all behavior, normalization, and oscillations at various time scales, can also possess memory in the absence of input. Previous work has shown that if you have a circuit with effective mutual inhibition between two pools representing different percepts and include some type of fatigue process such as synaptic depression or spike frequency adaptation, then the circuit exhibits various dynamics depending on the parameters and input conditions. If the inhibition strength is relatively low and the two pools receive equal inputs then the model will have a symmetric fixed point where both pools are equally active. As the inhibition strength (or input strength) increases, then there can be a bifurcation to oscillations between the two pools with a frequency that is dependent on the strengths of inhibition, recurrent excitation, input, and the time constant of the fatigue process. A further increase in inhibition leads to a bifurcation to a winner-take-all (WTA) state where one of the pools dominates the others. However, the same circuit would be expected to not possess "rivalry memory", where the same percept returns after the stimulus is completely removed for a duration that is long compared to the average oscillation period (dominance time). The reason is that during rivalry, the dominant pool is weakened while the suppressed pool is strengthened by the fatigue process. Thus when the stimulus is removed and returned, the suppressed pool would be expected to win the competition and become dominant. This reasoning had led people, including myself, to believe that rivalry memory could not be explained by this same model.
However, one thing Shashaank observed and that I hadn't really noticed before was that the winner-take-all state can persist for arbitrarily low input strength. We prove a little theorem in the paper showing that if the gain function (or FI curve) is concave (i.e. does not bend up), then the winner-take-all will persist for arbitrarily low input if the inhibition is strong enough. Most importantly, the input does not need to be tuned and could be provided by the natural background activity known to exist in the brain. Even zero mean noise is sufficient to maintain the WTA state. This low-activity WTA state can then serve as a memory since whatever was active during a state with strong input can remain active when the input is turned off and the neurons just receive low level background activity. It is thus a purely mutual inhibition maintained memory. We dubbed this "topological memory" because it is like a kink in the carpet that never disappears and persists over a wide range of parameter values and input strengths. Although, we only consider rivalry memory in this paper, the mechanism could also apply in other contexts such as working memory. In this paper, we also focus on a specific rivalry illusion called the quartet illusion, which makes the model slightly more complicated but we show how it naturally reduces to a two pool model. We are currently finishing a paper quantifying precisely how excitatory and inhibitory strengths affect rivalry and other cortical phenomena so watch this space. We also have submitted an abstract to neuroscience demonstrating how you can get WTA and rivalry in a balanced-state network.
Update: link to paper is fixed.
Ken Miller in the Times
October 17, 2015 October 17, 2015 Carson Chow Computational neuroscience, Neuroscience brain emulation
Read what computational neuroscientist Ken Miller of Columbia thinks about brain preservation and emulation. The piece captures Ken's tragic sense so perfectly.
Commentary on the Blue Brain Project
October 14, 2015 October 14, 2015 Carson Chow Computational neuroscience, Neuroscience Big Blue Brain
Definitely read Christof Koch and Michael Buice's commentary on the Blue Brain Project paper in Cell. They nicely summarize all the important points of the paper and propose a Turing Test for models. The performance of a model can be assessed by how long it would take an experimenter to figure out if the data from proposed neurophysiological experiments was coming from a model or the real thing. I think that this is a nice idea but there is one big difference between the Turing Test for artificial intelligence and brain simulations and that is that everyone has an innate sense of what it means to be human but no one knows what a real brain should be doing. In that sense, it is not really a Turing Test per se but rather the replication of experiments in a more systematic way than is done now. You do an experiment on a real brain then repeat it on the model and see if they get comparable results.
Big blue brain
October 12, 2015 Carson Chow Computational neuroscience, Neuroscience
Appearing in this week's edition of Cell is a paper summarizing the current status of Henry Markram's Blue Brain Project. You can download the paper for free until Oct 22 here. The paper reports on a morphological and electrophysiological statistically accurate reconstruction of a rat somatosensory cortex. I think it is a pretty impressive piece of work. They first did a survey of cortex (14 thousand recorded and labeled neurons) to get probability distributions for various types of neurons and their connectivities. The neurons are classified according to their morphology (55 m-types), electrophysiology (11 e-types), and synaptic dynamics (6 s-types). The neurons are connected according to an algorithm outlined in a companion paper in Frontiers in Computational Neuroscience that reproduces the measured connectivity distribution. They then created a massive computer simulation of the reconstructed circuit and show that it has interesting dynamics and can reproduce some experimentally observed behaviour.
Although much of the computational neuroscience community has not really rallied behind Markram's mission, I'm actually more sanguine about it now. Whether the next project to do the same for the human brain is worth a billion dollars, especially if this is a zero sum game, is another question. However, it is definitely a worthwhile pursuit to systematically catalogue and assess what we know now. Just like how IBM's Watson did not really invent any new algorithms per se, it clearly changed how we perceive machine learning by showing what can be done if enough resources are put into it. One particularly nice thing the project has done is to provide a complete set of calibrated models for all types of cortical neurons. I will certainly be going to their data base to get the equations for spiking neurons in all of my future models. I think one criticism they will face is that their model basically produced what they put in but to me that is a feature not a bug. A true complete description of the brain would be a joint probability distribution for everything in the brain. This is impossible to compute in the near future no matter what scale you choose to coarse grain over. No one really believes that we need all this information and thus the place to start is to assume that the distribution completely factorizes into a product of independent distributions. We should at least see if this is sufficient and this work is a step in that direction.
However, the one glaring omission in the current rendition of this project is an attempt to incorporate genetic and developmental information. A major constraint in how much information is needed to characterize the brain is how much is contained in the genome. How much of what determines a neuron type and its location is genetically coded, determined by external inputs, or is just random? When you see great diversity in something there are two possible answers: 1) the details matter a lot or 2) details do not matter at all. I would want to know the answer to this question first before I tried to reproduce the brain.
How the brain constructs the visual world
October 9, 2015 October 9, 2015 Carson Chow Computational neuroscience, Neuroscience, Pedagogy
First there was Carl Sagan, then there was Neil deGrasse Tyson, now there is Michael Buice:
Guest Post: On Proving Too Much in Scientific Data Analysis
August 13, 2015 August 14, 2015 Carson Chow Computational neuroscience, Neuroscience
I asked Rick Gerkin to write a summary of his recent eLife paper commenting on a much hyped Science paper on how many odours we can discriminate.
On Proving Too Much in Scientific Data Analysis
by Richard C. Gerkin
First off, thank you to Carson for inviting me to write about this topic.
Last year, Science published a paper by a group at Rockefeller University claiming that humans can discriminate at least a trillion smells. This was remarkable and exciting because, as the authors noted, there are far fewer than a trillion mutually discriminable colors or pure tones, and yet olfaction has been commonly believed to be much duller than vision or audition, at least in humans. Could it in fact be much sharper than the other senses?
After the paper came out in Science, two rebuttals were published in eLife. The first was by Markus Meister, an olfaction and vision researcher and computational neuroscientist at Cal Tech. My colleague Jason Castro and I had a separate rebuttal. The original authors have also posted a re-rebuttal of our two papers (mostly of Meister's paper), which has not yet been peer reviewed. Here I'll discuss the source of the original claim, and the logical underpinnings of the counterclaims that Meister, Castro, and I have made.
How did the original authors support their claim in the Science paper? Proving this claim by brute force would have been impractical, so the authors selected a representative set of 128 odorous molecules and then tested a few hundred random 30-component mixtures of those molecules. Since many mixture stimuli can be constructed in this way but only a small fraction can be practically tested, they tried to extrapolate their experimental results to the larger space of possible mixtures. They relied on a statistical transformation of the data, followed by a theorem from the mathematics of error-correcting codes, to estimate — from the data they collected — a lower bound on the actual number of discriminable olfactory stimuli.
The two rebuttals in eLife are mostly distinct from one another but have a common thread: both effectively identify the Science paper's analysis framework with the logical fallacy of `proving too much', which can be thought of as a form of reductio ad absurdum. An argument `proves too much' when it (or an argument of parallel construction) can prove things that are known to be false. For example, the 11th century theologian St. Anselm's ontological argument [ed note: see previous post] for the existence of god states (in abbreviated form): "God is the greatest possible being. A being that exists is greater than one that doesn't. If God does not exist, we can conceive of an even greater being, that is one that does exist. Therefore God exists". But this proves too much because the same argument can be used to prove the existence of the greatest island, the greatest donut, etc., by making arguments of parallel construction about those hypothetical items, e.g. "The Lost Island is the greatest possible island…" as shown by Anselm's contemporary Gaunilo of Marmoutiers. One could investigate further to identify more specific errors in logic in Anselm's argument, but this can be tricky and time-consuming. Philosophers have spent centuries doing just this, with varying levels of success. But simply showing that the argument proves too much is sufficient to at least call the conclusion into question. This makes `proves too much' a rhetorically powerful approach. In the context of a scientific rebuttal, leading with a demonstration that this fallacy has occurred piques enough reader interest to justify a dissection of more specific technical errors. Both eLife rebuttals use this approach, first showing that the analysis framework proves too much, and then exploring the source(s) of the error in greater detail.
How does one show that a particular detailed mathematical analysis `proves too much' about experimental data? Let me reduce the analysis in the Science paper to the essentials, and abstract away all the other mathematical details. The most basic claim in that paper is based upon what I will call `the analysis framework':
The authors did three basic things. First, they extracted a critical parameter from their data set using a statistical procedure I'll call . represents an average threshold for discriminability, corresponding to the number of components by which two mixtures must differ to be barely discriminable. Second, they fed this derived value, , into a function, that produces a number of odorous mixtures . Finally, they argued that the number so obtained necessarily underestimates the `true' number of discriminable smells, owing to the particular form of . Each step and proposition can be investigated:
1) How does the quantity behave as the data or form of varies? That is, is the `right thing' to do to the data?
2) What implicit assumptions does make about the sense of smell — are these assumptions reasonable?
3) Is the stated inequality — which says that any number derived using will always underestimate the true value z — really valid?
What are the rebuttals about? Meister's paper rejects the equation 2 on the grounds that is unjustified for the current problem. Castro and I are also critical of , but focus more on equations 1 and 3, criticizing the robustness of and demonstrating that the inequality should be reversed (the last of which I will not discuss further here). So together we called everything about the analysis framework into question. However, all parties are enthusiastic about the data itself, as well as its importance, so great care should be taken to distinguish the quality of the data from the validity of the interpretation.
In Meister's paper, he shows that the analysis framework proves too much by using simulations of simple models, using either synthetic data or the actual data from the original paper. These simulations show that the original analysis framework can generate all sorts of values for which are known to be false by construction. For example, he shows that a synthetic organism constructed to have 3 odor percepts necessarily produces data which, when the analysis framework is applied, yield values of . Since we know by construction that the correct answer is 3, the analysis framework must be flawed. This kind of demonstration of `proving too much' is also known by the more familiar term `positive control': a control where a specific non-null outcome can be expected in advance if everything is working correctly. When instead of the correct outcome the analysis framework produces an incredible outcome reminiscent of the one reported in the Science paper, then that framework proves too much.
Meister then explores the reason the equations are flawed, and identifies the flaw in . Imagine making a map of all odors, wherein similar-smelling odors are near each other on the map, and dissimilar-smelling odors are far apart. Let the distance between odors on the map be highly predictive of their perceptual similarity. How many dimensions must this map have to be accurate? We know the answer for a map of color vision: 3. Using only hue (H), saturation (S), and lightness (L) any perceptible color can be constructed, and any two nearby colors in an HSL map are perceptually similar, while any two distant colors in an such a map are perceptually dissimilar. The hue and saturation subspace of that map is familiar as the `color wheel', and has been understood for more than a century. In that map, hue is the angular dimension, saturation is the radial dimension, and lightness (if it were shown) would be perpendicular to the other two.
Meister argues that must be based upon a corresponding perceptual map. Since no such reliable map exists for olfaction, Meister argues, we cannot even begin to construct an for the smell problem; in fact, the actually used in the Science paper assumes a map with 128 dimensions, corresponding to the dimensionality of the stimulus not the (unknown) dimensionality of the perceptual space. By using such a high dimensional version of , a very high large value of is guaranteed, but unwarranted.
In my paper with Castro, we show that the original paper proves too much in a different way. We show that very similar datasets (differing only in the number of subjects, the number of experiments, or the number of molecules) or very similar analytical choices (differing only in the statistical significance criterion or discriminability thresholds used) produce vastly different estimates for , differing over tens of orders of magnitude from the reported value. Even trivial differences produce absurd results such as `all possible odors can be discriminated' or `at most 1 odor can be discriminated'. The differences were trivial in the sense that equally reasonable experimental designs and analyses could and have proceeded according to these differences. But the resulting conclusions are obviously false, and therefore the analysis framework has proved too much. This kind of demonstration of `proving too much' differs from that in Meister's paper. Whereas he showed that the analysis framework produces specific values that are known to be incorrect, we showed that it can produce any value at all under equally reasonable assumptions. For many of those assumptions, we don't know if the values it produces is correct or not; after all, there may be or or discriminable odors — we don't know. But if all values are equally justified, the framework proves too much.
We then showed the technical source of the error, which is a very steep dependence of on incidental features of the study design, mediated by , which is then amplified exponentially by a steep nonlinearity in . I'll illustrate with a much more well-known example from gene expression studies. When identifying genes that are thought to be differentially expressed in some disease or phenotype of interest, there is always a statistical significance threshold, e.g. , , etc. used for selection. After correcting for multiple comparisons, some number of genes pass the threshold and are identified as candidates for involvement in the phenotype. With a liberal threshold, e.g. , many candidates will be identified (e.g. 50). With a more moderate threshold, e.g. , fewer candidates will be identified (e.g. 10). With a more strict threshold, e.g. , still fewer candidates will be identified (e.g. 2). This sensitivity is well known in gene expression studies. We showed that the function in the original paper has a similar sensitivity.
Now suppose some researcher went a step further and said, "If there are candidates genes involved in inflammation, and each has two expression levels, then there are inflammation phenotypes". Then the estimate for the number of inflammation phenotypes might be:
at ,
at , and
at .
Any particular claim about the number of inflammation phenotypes from this approach would be arbitrary, incredibly sensitive to the significance threshold, and not worth considering seriously. One could obtain nearly any number of inflammation phenotypes one wanted, just by setting the significance threshold accordingly (and all of those thresholds, in different contexts, are considered reasonable in experimental science).
But this is essentially what the function does in the original paper. By analogy, is the thresholding step, is the number of candidate genes, and is the number of inflammation phenotypes. And while all of the possible values for in the Science paper are arbitrary, a wide range of them would have been unimpressively small, another wide range would have been comically large, and only the `goldilocks zone' produced the impressive but just plausible value reported in the paper. This is something that I think can and does happen to all scientists. If your first set of analysis decisions gives you a really exciting result, you may forget to check whether other reasonable sets of decisions would give you similar results, or whether instead they would give you any and every result under the sun. This robustness check can prevent you from proving too much — which really means proving nothing at all.
2015-08-14: typos fixed
Talk in Göttingen
June 3, 2015 June 3, 2015 Carson Chow Computational neuroscience, Kinetic Theory, Talks
I'm currently in Göttingen, Germany at the Bernstein Sparks Workshop: Beyond mean field theory in the neurosciences, a topic near and dear to my heart. The slides for my talk are here. Of course no trip to Göttingen would be complete without a visit to Gauss's grave and Max Born's house. Photos below.
New paper on path integrals
March 24, 2015 Carson Chow Computational neuroscience, Kinetic Theory, Papers, Probablity
Carson C. Chow and Michael A. Buice. Path Integral Methods for Stochastic Differential Equations. The Journal of Mathematical Neuroscience, 5:8 2015.
Abstract: Stochastic differential equations (SDEs) have multiple applications in mathematical neuroscience and are notoriously difficult. Here, we give a self-contained pedagogical review of perturbative field theoretic and path integral methods to calculate moments of the probability density function of SDEs. The methods can be extended to high dimensional systems such as networks of coupled neurons and even deterministic systems with quenched disorder.
This paper is a modified version of our arXiv paper of the same title. We added an example of the stochastically forced FitzHugh-Nagumo equation and fixed the typos.
Jobs at the Allen Institute
March 7, 2015 Carson Chow Computational neuroscience, Science
The Allen Institute for Brain Science is currently recruiting. The positions are listed on this link but in particular see below:
SCIENTIST I – MODELING, ANALYSIS AND THEORY
The Modeling, Analysis and Theory team at the Allen Institute is seeking a candidate with strong mathematical and computational skills who will work closely with both the team as well as experimentalists in order to both maximize the potential of datasets as well as realize that potential via analysis and theory. The successful candidate will be expected to develop analysis for populations of neurons as well as establish theoretical results on cortical computation, object recognition, and related areas in order to aid the Institute in understanding the most complex piece of matter in the universe.
Michael Buice, Scientist II
michaelbu@ alleninstitute.org
The tragic life of Walter Pitts
February 9, 2015 Carson Chow Computational neuroscience, Computer Science
Everyone in computational neuroscience knows about the McCulloch-Pitts neuron model, which forms the foundation for neural network theory. However, I never knew anything about Warren McCulloch or Walter Pitts until I read this very interesting article in Nautilus. I had no idea that Pitts was a completely self-taught genius that impressed the likes of Bertrand Russell, Norbert Wiener and John von Neumann but was also a self-destructive alcoholic. One thing the article nicely conveys was the camaraderie and joie de vivre that intellectuals experienced in the past. Somehow this spirit seems missing now.
Sebastian Seung and the Connectome
January 11, 2015 January 12, 2015 Carson Chow Computational neuroscience, Neuroscience, Philosophy
The New York Times Magazine has a nice profile on theoretical neuroscientist Sebastian Seung this week. I've known Sebastian since we were graduate students in Boston in the 1980's. We were both physicists then and both ended up in biology though through completely different paths. The article focuses on his quest to map all the connections in the brain, which he terms the connectome. Near the end of the article, neuroscientist Eve Marder of Brandeis comments on the endeavor with the pithy remark that "If we want to understand the brain, the connectome is absolutely necessary and completely insufficient." To which the article ends with
Seung agrees but has never seen that as an argument for abandoning the enterprise. Science progresses when its practitioners find answers — this is the way of glory — but also when they make something that future generations rely on, even if they take it for granted. That, for Seung, would be more than good enough. "Necessary," he said, "is still a pretty strong word, right?"
Personally, I am not sure if the connectome is necessary or sufficient although I do believe it is a worthy task. However, my hesitation is not because of what was proposed in the article, which is that we exist in a fluid world and the connectome is static. Rather, like Sebastian, I do believe that memories are stored in the connectome and I do believe that "your" connectome does capture much of the essence of "you". Many years ago, the CPU on my computer died. Our IT person swapped out the CPU and when I turned my computer back on, it was like nothing had happened. This made me realize that everything about the computer that was important to me was stored on the hard drive. The CPU didn't matter even though every thing a computer did relied on the CPU. I think the connectome is like the hard drive and trying to figure out how the brain works from it is like trying to reverse engineer the CPU from the hard drive. You can certainly get clues from it such as information is stored in binary form but I'm not sure if it is necessary or sufficient to figure out how a computer works by recreating an entire hard drive. Likewise, someday we may use the connectome to recover lost memories or treat some diseases but we may not need it to understand how a brain works.
How does the cortex compute?
November 5, 2014 November 5, 2014 Carson Chow Computational neuroscience, Neuroscience
Gary Marcus, Adam Marblestone, and Thomas Dean have an opinion piece in Science this week challenging the notion of the "canonical cortical circuit". They have a longer and open version here. Their claim is that the cortex is probably doing a variety of different computations, which they list in their longer paper. The piece has prompted responses by a number of people including Terry Sejnowski and Stephen Grossberg on the connectionist listserv (Check the November archive here).
What's wrong with neuroscience
November 2, 2014 Carson Chow Computational neuroscience, Neuroscience, Opinion
Here is a cute parable in Frontiers in Neuroscience from cognitive scientist Joshua Brown at Indiana Univeristy. It mirrors a lot of what I've been saying for the past few years:
The tale of the neuroscientists and the computer: Why mechanistic theory matters
http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00349/full
A little over a decade ago, a biologist asked the question "Can a biologist fix a radio?" (Lazebnik, 2002). That question framed an amusing yet profound discussion of which methods are most appropriate to understand the inner workings of a system, such as a radio. For the engineer, the answer is straightforward: you trace out the transistors, resistors, capacitors etc., and then draw an electrical circuit diagram. At that point you have understood how the radio works and have sufficient information to reproduce its function. For the biologist, as Lazebnik suggests, the answer is more complicated. You first get a hundred radios, snip out one transistor in each, and observe what happens. Perhaps the radio will make a peculiar buzzing noise that is statistically significant across the population of radios, which indicates that the transistor is necessary to make the sound normal. Or perhaps we should snip out a resistor, and then homogenize it to find out the relative composition of silicon, carbon, etc. We might find that certain compositions correlate with louder volumes, for example, or that if we modify the composition, the radio volume decreases. In the end, we might draw a kind of neat box-and-arrow diagram, in which the antenna feeds to the circuit board, and the circuit board feeds to the speaker, and the microphone feeds to the recording circuit, and so on, based on these empirical studies. The only problem is that this does not actually show how the radio works, at least not in any way that would allow us to reproduce the function of the radio given the diagram. As Lazebnik argues, even though we could multiply experiments to add pieces of the diagram, we still won't really understand how the radio works. To paraphrase Feynmann, if we cannot recreate it, then perhaps we have not understood it (Eliasmith and Trujillo, 2014; Hawking, 2001).
Lazebnik's argument should not be construed to disparage biological research in general. There are abundant examples of how molecular biology has led to breakthroughs, including many if not all of the pharmaceuticals currently on the market. Likewise, research in psychology has provided countless insights that have led to useful interventions, for instance in cognitive behavioral therapy (Rothbaum et al., 2000). These are valuable ends in and of themselves. Still, are we missing greater breakthroughs by not asking the right questions that would illuminate the larger picture? Within the fields of systems, cognitive, and behavioral neuroscience in particular, I fear we are in danger of losing the meaning of the Question "how does it work"? As the saying goes, if you have a hammer, everything starts to look like a nail. Having been trained in engineering as well as neuroscience and psychology, I find all of the methods of these disciplines useful. Still, many researchers are especially well-trained in psychology, and so the research questions focus predominantly on understanding which brain regions carry out which psychological or cognitive functions, following the established paradigms of psychological research. This has resulted in the question being often reframed as "what brain regions are active during what psychological processes", or the more sophisticated "what networks are active", instead of "what mechanisms are necessary to reproduce the essential cognitive functions and activity patterns in the system." To illustrate the significance of this difference, consider a computer. How does it work?
**The Tale**
Once upon a time, a group of neuroscientists happened upon a computer (Carandini, 2012). Not knowing how it worked, they each decided to find out how it sensed a variety of inputs and generated the sophisticated output seen on its display. The EEG researcher quickly went to work, putting an EEG cap on the motherboard and measuring voltages at various points all over it, including on the outer case for a reference point. She found that when the hard disk was accessed, the disk controller showed higher voltages on average, and especially more power in the higher frequency bands. When there was a lot of computation, a lot of activity was seen around the CPU. Furthermore, the CPU showed increased activity in a way that is time-locked to computational demands. "See here," the researcher declared, "we now have a fairly temporally precise picture of which regions are active, and with what frequency spectra." But has she really understood how the computer works?
Next, the enterprising physicist and cognitive neuroscientist came along. "We don't have enough spatial resolution to see inside the computer," they said. So they developed a new imaging technique by which activity can be measured, called the Metabolic Radiation Imaging (MRI) camera, which now measures the heat (infrared) given off by each part of the computer in the course of its operations. At first, they found simply that lots of math operations lead to heat given off by certain parts of the CPU, and that memory storage involved the RAM, and that file operations engaged the hard disk. A flurry of papers followed, showing that the CPU and other areas are activated by a variety of applications such as word-processing, speech recognition, game play, display updating, storing new memories, retrieving from memory, etc.
Eventually, the MRI researchers gained a crucial insight, namely that none of these components can be understood properly in isolation; they must understand the network. Now the field shifts, and they begin to look at interactions among regions. Before long, a series of high profile papers emerge showing that file access does not just involve the disks. It involves a network of regions including the CPU, the RAM, the disk controller, and the disk. They know this because when they experimentally increase the file access, all of these regions show correlated increases in activity. Next, they find that the CPU is a kind of hub region, because its activity at various times correlates with activity in other regions, such as the display adapter, the disk controller, the RAM, and the USB ports, depending on what task they require the computer to perform.
Next, one of the MRI researchers has the further insight to study the computer while it is idle. He finds that there is a network involving the CPU, the memory, and the hard disk, as (unbeknownst to them) the idle computer occasionally swaps virtual memory on and off of the disk and monitors its internal temperature. This resting network is slightly different across different computers in a way that correlates with their processor speed, memory capacity, etc., and thus it is possible to predict various capacities and properties of a given computer by measuring its activity pattern when idle. Another flurry of publications results. In this way, the neuroscientists continue to refine their understanding of the network interactions among parts of the computer. They can in fact use these developments to diagnose computer problems. After studying 25 normal computers and comparing them against 25 computers with broken disk controllers, they find that the connectivity between the CPU and the disk controller is reduced in those with broken disk controllers. This allows them to use MRI to diagnose other computers with broken disk controllers. They conclude that the disk controller plays a key role in mediating disk access, and this is confirmed with a statistical mediation analysis. Someone even develops the technique of Directional Trunk Imaging (DTI) to characterize the structure of the ribbon cables (fiber tract) from the disk controller to the hard disk, and the results match the functional correlations between the hard disk and disk controller. But for all this, have they really understood how the computer works?
The neurophysiologist spoke up. "Listen here", he said. "You have found the larger patterns, but you don't know what the individual circuits are doing." He then probes individual circuit points within the computer, measuring the time course of the voltage. After meticulously advancing a very fine electrode in 10 micron increments through the hard material (dura mater) covering the CPU, he finds a voltage. The particular region shows brief "bursts" of positive voltage when the CPU is carrying out math operations. As this is the math co-processor unit (unbeknownst to the neurophysiologist), the particular circuit path is only active when a certain bit of a floating point representation is active. With careful observation, the neurophysiologist identifies this "cell" as responding stochastically when certain numbers are presented for computation. The cell therefore has a relatively broad but weak receptive field for certain numbers. Similar investigations of nearby regions of the CPU yield similar results, while antidromic stimulation reveals inputs from related number-representing regions. In the end, the neurophysiologist concludes that the cells in this particular CPU region have receptive fields that respond to different kinds of numbers, so this must be a number representation area.
Finally the neuropsychologist comes along. She argues (quite reasonably) that despite all of these findings of network interactions and voltage signals, we cannot infer that a given region is necessary without lesion studies. The neuropsychologist then gathers a hundred computers that have had hammer blows to various parts of the motherboard, extension cards, and disks. After testing their abilities extensively, she carefully selects just the few that have a specific problem with the video output. She finds that among computers that don't display video properly, there is an overlapping area of damage to the video card. This means of course that the video card is necessary for proper video monitor functioning. Other similar discoveries follow regarding the hard disks and the USB ports, and now we have a map of which regions are necessary for various functions. But for all of this, have the neuroscientists really understood how the computer works?
**The Moral**
As the above tale illustrates, despite all of our current sophisticated methods, we in neuroscience are still in a kind of early stage of scientific endeavor; we continue to discover many effects but lack a proportionally strong standard model for understanding how they all derive from mechanistic principles. There are nonetheless many individual mathematical and computational neural models. The Hodgkin-Huxley equations (Hodgkin and Huxley, 1952), Integrate-and-fire model (Izhikevich, 2003), Genesis (Bower and Beeman, 1994), SPAUN (Eliasmith et al., 2012), and Blue Brain project (Markram, 2006) are only a few examples of the models, modeling toolkits, and frameworks available, besides many others more focused on particular phenomena. Still, there are many different kinds of neuroscience models, and even many different frameworks for modeling. This means that there is no one theoretical lingua franca against which to evaluate empirical results, or to generate new predictions. Instead, there is a patchwork of models that treat some phenomena, and large gaps where there are no models relevant to existing phenomena. The moral of the story is not that the brain is a computer. The moral of the story is twofold: first, that we sorely need a foundational mechanistic, computational framework to understand how the elements of the brain work together to form functional units and ultimately generate the complex cognitive behaviors we study. Second, it is not enough for models to exist—their premises and implications must be understood by those on the front lines of empirical research.
**The Path Forward**
A more unified model shared by the community is not out of reach for neuroscience. Such exists in physics (e.g. the standard model), engineering (e.g. circuit theory), and chemistry. To move forward, we need to consider placing a similar level of value on theoretical neuroscience as for example the field of physics places on theoretical physics. We need to train neuroscientists and psychologists early in their careers in not just statistics, but also in mathematical and computational modeling, as well as dynamical systems theory and even engineering. Computational theories exist (Marr, 1982), and empirical neuroscience is advancing, but we need to develop the relationships between them. This is not to say that all neuroscientists should spend their time building computational models. Rather, every neuroscientist should at least possess literacy in modeling as no less important than, for example, anatomy. Our graduate programs generally need improvement on this front. For faculty, if one is in a soft money position or on the tenure clock and cannot afford the time to learn or develop theories, then why not collaborate with someone who can? If we really care about the question of how the brain works, we must not delude ourselves into thinking that simply collecting more empirical results will automatically tell us how the brain works any more than measuring the heat coming from computer parts will tell us how the computer works. Instead, our experiments should address the questions of what mechanisms might account for an effect, and how to test and falsify specific mechanistic hypotheses (Platt, 1964).
Julia vs Python update
August 19, 2014 Carson Chow Computational neuroscience, Computer Science
We've been using Julia for a little over a month now and I am quite pleased thus far. It is fast enough for us to get decent results for our MCMC runs; runs for which Python and Brian were too slow. Of course, we probably could have tried to optimize our other codes but Julia installs right out of the box and is very easy to program in. We still have issues for plotting but do have minimal plot functionality using PyCall and importing matplotlib.pyplot. One can also dump the results to a file and have either Python or some other software make the plots. I already find myself reaching for Julia when I want to write a quick code to solve a problem instead of relying on Matlab like I used to. While I would like to try PyDSTool, the lack of a trivial installation is holding us back. For you budding software developers out there, if it takes more than one click on a link to make it work on my computer, I am likely to go to something else. The reason I switched from Linux to Mac a decade ago was that I wanted Unix capability and the ability to print without having to consult with a sys admin.
Numerical integration
August 14, 2014 Carson Chow Computational neuroscience, Computer Science, Mathematics
Solving differential equations numerically is a very mature field with multiple algorithms carefully explained in multiple textbooks. However, when it comes down to actually solving them in practice by nonspecialists, a lot of people, myself included, will often resort to the Euler method. There are two main reasons for this. The first is that it is trivial to remember and code and the second is that Moore's law has increased the speed of computing sufficiently that we can get away with it. However, I think it's time to get out of the eighteenth century and at least move into the nineteenth.
Suppose you have a differential equation , where can be a scalar or a vector in an arbitrary number of dimensions. The Euler method simply discretizes the time derivative via
so that at time step is given by
Now what could be simpler than that? There are two problems with this algorithm. The first is that it is only accurate to order so you need to have very small steps to simulate accurately. The second is that the Euler method is unstable if the step size is too large. The way around these limitations is to use very small time steps, which is computationally expensive.
The second most popular algorithm is probably (4th order) Runge-Kutta, which can generally use larger time steps for the same accuracy. However, a higher order algorithm is not necessarily faster because what really slows down a numerical scheme is the number of function evaluations it needs to make and 4th order Runge-Kutta needs to make a lot of function evaluations. Runge-Kutta is also plagued by the same stability problems as Euler and that can also limit the maximum allowed step size.
Now, these problems have been known for decades if not centuries and work-arounds do exist. In fact, the most sophisticated solvers such as CVODE tend to use adaptive multi-step methods, like Adams-Bashforth (AB) or it's implicit cousin Adams-Moulton and this is what we should all be implementing in our own codes. Below I will give simple pseudo-code to show how a 2nd order Adams-Bashforth scheme is not much more complicated to code than Euler and uses the same number of function evaluations. In short, with not much effort, you can speed your code immensely by simply switching to AB.
The cleverness of AB is that it uses previous steps to refine the estimate for the next step. In this way, you get a higher order scheme without having to re-evaluate the right hand side of your ODE. You simply reuse your computations. It's the ultimate green scheme. For the same ODE example above 2nd order AB is simply
You just need to store the previous two evaluations to compute the next step. To start the algorithm, you can just use an Euler algorithm to get the first step.
Here is the 2nd order AB algorithm written in pseudo Julia code for a one dimensional version of the ODE above.
# make x a two dimensional vector for the two time steps
# Set initial condition
x[1]=0;
# Store evaluation of function f(x,t) for later use
fstore[1]= f(x[1],h)
# Take Euler step
x[2] = x[1] + h*fstore[1]
# store function at time = 2h
fstore[2] = f(x[2],2*h)
# Store computed values for output
xout[1]=x[1]
# Take AB steps
for t = 3:Tfinal
# set update indices for x based on parity of t
index2 = t%2+1
index1 = (t-1)%2+1
# 2nd order AB step
x[index1] = x[index2]+h*(1.5*fstore[2]-0.5*fstore[1]))
# update stored function
fstore[index1] = f(x[index1],t*h)
xout[t]=x[index1]
return xout
The neuro software gap
August 4, 2014 August 5, 2014 Carson Chow Computational neuroscience
Our foray into Julia was mostly motivated by a desire to have a programming language as fast as C but as easy to program as Matlab. In particular, we need really fast looping speed, which is necessary to simulate dynamical systems. Our particular project involves fitting a conductance-based model (i.e. Hodgkin-Huxley-like equations) to some data using MCMC. We're not trying to fit the precise spike shapes per se but the firing rates of synaptically coupled pools of neurons in response to various inputs. We thus need to run simulations for some time, compare the averaged rates to previous runs, and iterate. For good fits, we may need to do this a million times. Hence, we need speed and we can't really parallelize because we need information from one iteration before going to the next.
Now, one would think that simulating neural networks is a long solved problem and I thought that too. Hence, I instructed my fellow Wally Xie to look into all the available neural simulators and see what fits the bill. We first tried Neuron but couldn't get all of it to work on our Linux machine running CentOS (which is what NIH forces us to use). That was the end of Neuron for us. We next tried Nest but found it difficult to specify the neuron model. We finally settled on Brian, which is adaptable, written in Python, and supported by very nice people, but is turning out to be too slow. Native Python is also too slow. Stan would be great but it does not yet support differential equations. Wally is now implementing the code in Julia, which is turning out to be very fast but has a possible bug that is preventing long runs. The developers are extremely helpful though so we believe this will be worked out soon. However, it is hard to get plotting to work on Julia and as pointed out by many, Julia is not nearly as complete as Python or Matlab. (Yes, Matlab is too slow). We could use C and will go to it next if Julia does not work out. Thus, even though people have been developing neural simulators for decades, there is still an unmet need for something adaptable, fast, easy to use, and runs out of the box. A neuro-Stan would be great.
Talk at Jackfest
July 16, 2014 July 19, 2014 Carson Chow Computational neuroscience, Conferences, Kinetic Theory, Talks
I'm currently in Banff, Alberta for a Festschrift for Jack Cowan (webpage here). Jack is one of the founders of theoretical neuroscience and has infused many important ideas into the field. The Wilson-Cowan equations that he and Hugh Wilson developed in the early seventies form a foundation for both modeling neural systems and machine learning. My talk will summarize my work on deriving "generalized Wilson-Cowan equations" that include both neural activity and correlations. The slides can be found here. References and a summary of the work can be found here. All videos of the talks can be found here.
Addendum: 17:44. Some typos in the talk were fixed.
Addendum: 18:25. I just realized I said something silly in my talk. The Legendre transform is an involution because the transform of the transform is the inverse. I said something completely inane instead.
The need for speed
July 1, 2014 Carson Chow Computational neuroscience, Computer Science
Thanks for all the comments about the attributes of Python and Julia. It seems to me that the most prudent choice is to learn Python and Julia. However, what I would really like to know is just how fast these languages really are and here is the test. What I want to do is to fit networks of coupled ODEs (and PDEs) to data using MCMC (see here). This means I need a language that loops fast. An example in pseudo-Matlab code would be
for n = 1:N
for i = 1:T
y(i+1) = M\y(i)
Compare to data and set new parameters
where h is a parameter and M is some matrix (say 1000 dimensional), which is sometimes a Toeplitz matrix but not always. Hence, in each time step I need to invert a matrix, which can depend on time so I can't always precompute, and do a matrix multiplication. Then in each parameter setting step I need to sum an objective function like the mean square error over all the data points. The code to do this in C or Fortran can be pretty complicated because you have to keep track of all the indices and call linear algebra libraries. I thus want something that has the simple syntax of Matlab but is as fast as C. Python seems to be too slow for our needs but maybe we haven't optimized the code. Julia seems like the perfect fit but let me know if I am just deluded.


Native American villages as previous as from 14,000 BC make this a rich assortment of New World history and significantly Northeastern US chronicling. many unique installations are replicated nowhere outside Pennsylvania. The listing beneath finds them in all 4 corners of the state in addition to centrally located.
I even have seen a few of these websites and I'm disgusted. I'm 14, I was a preteen mannequin and I NEVER did any provocotive posses EVER. I wasn't even allowed to model swimwear until I was thirteen. I find these young girls being taken benefit of disgusting; I'm frightened how their mother and father let them do it and if they even knew their mother and father knew this was occurring. I'm still a teen mannequin and I won't ever do any provocative poses until I'm 18 and I may still not want too. I assume such sites needs to be shut down and homeowners prosecuted.
One of the problems one will encounter when looking out the online for a authentic modeling company is just not understanding what is going to open when clicking a hyperlink. Some pages offer a number of hyperlinks that are placed on a web page as banners. The footage on MOST of those banners are disturbing in the way in which that they provide photos of youngster or preteen ladies in thongs or underwear in very provocative poses.
What lots of you guys do not understand is that the little women in these web sites aren't your common trying women. They have exceptional bodily attributes that resemble a mature womanly figure and beautiful doll-like faces. It is really rare to seek out little girls with this excellent mix of options. Apparently, you all have not seen it for yourself however with shut-up pictures of their figures, your can hardly tell the difference between an adult and a little bit girls image. It is this mixture of mature and childlike features that make them so appealing, and a really uncommon and precious gem.
The first thing it's important to do is to purchase the area and the hosting. This is the initial step and you have to try this in either you need to become an affiliate or you need to have your personal website. After that design your website and make it reside. I know the website can be with out search engine and you'll't make online searches for flights, resorts or holidays.


No one likes exams. Even if you are extremely well prepared, as the exams approach you are bound to feel a little scared. And if you know that you are not as well prepared as you should be – you can feel like you are living in a nightmare.
There is no doubt that regular study – with a systematic approach to every subject and lots and lots of practice is the best way to prepare for exams. However, when you are just a few weeks away from exams – instead of crying about what you haven't done and drowning in anxiety by imagining extreme situations and worst case scenarios, the smart thing to do is – to utilise the time you have left effectively and do the best you can.
The brain is the one organ in your body that you are going to need the most to do well in your exams and it must be kept in top notch condition. Nutritious food and adequate sleep are the two things that the brain really needs to function optimally – but these are usually the first things to be sacrificed when there is a lot to study.
Do not neglect meals. Eating nutrient rich meals at regular intervals is the key to remembering all that you study. Remember – complex carbohydrates, proteins, fats, vitamins and minerals are all essential. Restrictive diets or junk food diets are extremely harmful for brains that are trying to learn and remember.
Sleep is just as essential because a tired brain cannot learn and remember like it should.
Fear makes us run away from what is scary and so we often tend to neglect subjects that we are weak in. This is a big mistake.
The board exam results are an aggregate percentage of all your subjects and doing very badly in one subject can ruin your overall result – even if you have done extremely well in the other subjects. Make sure you study all the subjects and all the chapters of each subject and don't ignore diagrams, tables and graphs.
Allocate time to each subject depending on how much syllabus there is and how good you are at that subject.
When the exams are around four weeks away - start preparing for them in the reverse order. Prepare first for the last exam and plan it such - that you start preparing for the first exam about 2 weeks before the date.
Remember that quizzing yourself on what you have learnt is a more effective way of revising than simply rereading everything repeatedly.
Use question papers of the previous years to revise as it is likely that several questions may be repeated.
Also, study in 3 hour slots so that it helps you practice the intense concentration that you will need during your exams which will be of the same duration.
The stressed brain does not work well.
While it is impossible not to feel stressed about exams - it is important to actively try to minimise the stress. One of the best ways to reduce the fear of what may happen in the future – is to plunge into action in the present moment. And therefore the only way to reduce exam stress is to study.
Stop comparing yourself with others. What another person is doing, has done or may do in the future is not going to change your result. Concentrate on doing what you can do. Forget about others.
If you feel the stress mounting take a break from studying and go for a short run or walk. If that does not help speak to a parent or teacher who you think may be able to help.
However much or however little you may know in a subject – what you write in the exam and how well you present it - is what really matters. Read the question carefully, answer to the point and write neatly without cutting and scribbling.
Always get enough sleep before your exams. And never go to the exam hall on an empty stomach.
Put your best foot forward as you prepare for your exams and tackle them, but remember that a poor result is not the end of the road. Exams are just a sample of the challenges that will be thrown to you later in life – they are designed to prepare you to meet these challenges.
Thoughts determine action – action determines outcome. Always remain positive.


text:
Il Cantone di Segonzac era una divisione amministrativa dell'Arrondissement di Cognac.

A seguito della riforma approvata con decreto del 20 febbraio 2014, che ha avuto attuazione dopo le elezioni dipartimentali del 2015, è stato soppresso.

Composizione
Comprendeva i comuni di:
 Ambleville
 Angeac-Champagne
 Bourg-Charente
 Criteuil-la-Magdeleine
 Gensac-la-Pallue
 Genté
 Gondeville
 Juillac-le-Coq
 Lignières-Sonneville
 Mainxe
 Saint-Fort-sur-le-Né
 Saint-Même-les-Carrières
 Saint-Preuil
 Salles-d'Angles
 Segonzac
 Verrières

Note

Collegamenti esterni
Statistiche sul sito dell'INSEE

Segonzac

redpajama_set_name: RedPajamaWikipedia


text:
Back to Community services
Hawthorne Intermediate Care Unit (HICU) provides intermediate care for rehabilitation and step-up beds for patients requiring short-term inpatient care.
The unit provides:
Intermediate care beds for patients requiring a full multidisciplinary rehabilitation programme
Step-up beds for short-term inpatient care and to prevent acute hospital admission (accessed via Hillingdon Rapid Response Service)
The unit is nurse-led and is staffed by a multi-disciplinary team including a dedicated ward pharmacist, physiotherapist, occupational therapist, nurses, and activities coordinator. Link nurses for tissue viability, continence, and infection prevention and control provide support advice and training for the unit.
Medical cover for the ward is provided by The Hillingdon Hospitals Care of the Elderly Team and Hillingdon Rapid Response Service work closely with the service to ensure patients receive well-planned and facilitated discharges to enable seamless transfer back into the community.
Patients must be registered with a Hillingdon general practitioner (GP) and live in Hillingdon.
How to be referred to this service
The service accepts referrals from GPs or other healthcare professionals by telephone direct to the service on 01895 891238 or 01895 891239.
The service operates 24 hours a day, seven days a week, throughout the year.
Visiting hours are from 14:00-17:00 and 18:00-20:00.
Bus: U1, U2, U3, U4, U5, U7
Train: Yiewsley
Tube: Uxbridge and Hillingdon (Metropolitan and Piccadilly lines)
Parking: Pay and display
Help in a crisis
If you need help in a crisis, please do the following
Contact the CNWL service looking after you during their office hours
If the crisis is during when your service is closed, there are alternative options provided by the NHS found on this page.
If you are not already being looked after by CNWL or elsewhere within the NHS, see your GP during normal working hours as they can then refer you to a local service.
Find your closest GP on NHS Choices.
The Woodlands Centre
Hillingdon Hospital
Pield Heath Road
UB8 3NN


text:
Q: Chrome Extension No-Refresh I am looking to have my chrome extension running even when the popup is not shown. I have a basic popup.html file that displays the extension. Then I have several javascript files that need to be running constantly. The extension is essentially a timer that keeps track of how long it has been running until you stop it. The problem is that it works until you close the extension to do something else in chrome. I am looking to have it continue to count time even when the popup is not being shown.
**My manifest.json includes: "persistent": true;
Do I need to have my scripts in the background.js file?

redpajama_set_name: RedPajamaStackExchange


Caps & Flints is the journal of the Antique & Historical Arms Collectors Guild of Victoria and has been published continuously since 1966.
The journal is one of the many benefits of membership of the Guild and features a range of interesting articles and research by Guild members and guest authors on various aspects of arms, accoutrements and militaria.
The presentation ofCaps & Flints since its first edition more than forty years ago has changed markedly, and in 2008 a new cover design was introduced; a percusor to further editorial revamps.
However, the journal's central aim of presenting leading arms research for the Australian collector remains a consistent expression of the Guild's endeavours.
This is a cumulative index of the articles that have appeared in Caps & Flints since it was first published 34 years ago.
A major theme of the articles that have been published in Caps & Flints over the years is the study of Australian arms and militaria.
It is felt that the index will provide a useful research tool for students of Australian military history, as well as being a useful reference for the collector.
But in saying this it must also be pointed out that Caps & Flints has published many valuable articles on firearms and militaria matters not connected with Australian history. These are commended to researchers as well.
The index has been arranged alphabetically in three main sections.
In addition indices of regular segments have been provided.
Unfortunately we are unable to provide individual copies of back issues, but we can provide a copy of the requested article for a nominal fee to cover copying, postage and handling.


text:
An official selection at the Cannes Film Festival, writer/director Michael O'Shea's debut feature The Transfiguration follows troubled teen Milo who hides behind his fascination with vampire lore. When he meets the equally alienated Sophie, the two form a bond that begins to challenge Milo's dark obsession, blurring his fantasy into reality. A chilling portrait of violence, The Transfiguration is an atmospheric thriller set against the grit of New York City.
"Engaging!"
– Variety
"A quiet bite, but one that leaves its mark"
– David Rooney, The Hollywood Reporter
"An impressively confident directorial debut… a vampire movie that looks, feels, walks and talks like a gritty US indie flick"
– Charles Gant, ScreenDaily
Michael O'Shea
Susan Leber
Eric Ruffin
Chloe Levine
Format: DCP/2.39/Color
Sound Format: 5.1 Dolby Digital
Genre: Drama/Horror
See this film at the following theaters:
03/10/17 SXSW Film Festival (North American Premiere!) Austin, TX
03/30/17 RiverRun Int'l Film Festival Winston-Salem, NC
04/03/17 Athens Film Festival Athens, OH
04/05/17 San Francisco Int'l Film Festival San Francisco, CA
04/06/17 Chattanooga Film Festival Chattanooga, TN
04/06/17 IHSFF Phoenix, AZ
04/07/17 Angelika Film Center & Cafe New York, NY
04/08/17 Cleveland Int'l Film Festival Cleveland, OH
04/16/17 PhilaMOCA Philadelphia, PA
04/21/17 Landmark's Nuart Theatre Los Angeles, CA
04/21/17 Walker Art Center Minneapolis, MN
04/21/17 Angelika Pop-Up Washington D.C.
04/28/17 Alamo Drafthouse- Ritz Austin, TX
04/28/17 Kiggins Theatre Vancouver, WA (Portland area)
04/28/17 Gateway Film Center Columbus, OH
04/28/17 Screenland Crossroads at Tapcade Kansas City, MO
04/28/17 Montclair Film Festival Montclair, NJ
04/28/17 Zeitgeist Arts Center New Orleans, LA
04/28/17 Grand Illusion Cinema Seattle, WA
04/28/17 Grail Moviehouse Asheville, NC
05/03/17 Guild Cinema Albuquerque, NM
05/03/17 IndieEvents Sioux Falls, SD
05/05/17 Digital Gym/Media Arts Ctr San Diego, CA
05/05/17 Zoetropolis Lancaster, PA
05/11/17 River Street Theatre Jaffrey, NH
05/12/17 Normal Theatre Normal, IL
05/12/17 Syndicated Bar Theatre Kitchen Brooklyn, NY
05/18/17 Moogfest Durham, NC
05/19/17 Osio Theater Monterey, CA
05/27/17 Alamo Drafthouse – Yonkers Yonkers, NY
06/09/17 Jean Cocteau Cinema Santa Fe, NM
06/16/17 Roxie Cinema San Francisco, CA
06/16/17 Sie Film Center Denver, CO
06/16/17 Hollywood Theater Pittsburgh, PA
07/05/17 Malco Studio on the Square Memphis, TN
07/07/17 Acme Screening Room Lambertville, NJ
07/14/17 Indy Film Festival Indianapolis, IN
07/14/17 Celebration Cinemas – Grand Rapids, MI
07/28/17 New Parkway Theatre Oakland, CA
07/30/17 Lone Star Film Society Fort Worth, TX
09/16/17 Film Scene Iowa City, IA
09/29/17 Majestic Theatre Crested Butte, CO
10/24/17 West Virginia Film Festival Charleston, WV
10/28/17 UCR ARTSblock Riverside, CA
Strange Color Of Your Body's Tears, The
Belgium, Dutch, DVD/On-Demand, Erotic, France, French, Horror, Luxembourg, Mystery, Non-Theatrical, Thriller
Belgium | Hélène Cattet & Bruno Forzani
Empty Hours, The
Drama, DVD/On-Demand, France, Mexico, Non-Theatrical, Spain, Spanish
Mexico | Aarón Fernández
Wolf at the Door, A
Brazil, Drama, DVD/On-Demand, Non-Theatrical, Portuguese, Thriller
Brazil I Fernando Coimbra


A female landscape-gardener is awarded the esteemed assignment to construct the grand gardens at Versailles, a gilt-edged position which thrusts her to the very centre of the court of King Louis XIV.
Discover the exquisite and matchless beauty of the Palace of Versailles on an exclusive VIP tour. Pass through the velvet ropes with unprecedented access to the magnificent private Royal Quarter rooms, including Louis XVI's library, which are normally off-limits to the general public. The magic continues in the glorious Gardens of Versailles, a tranquil and romantic meander through blooms and boughs. Wander back to the Palace and enjoy the magnificent Musical Fountains Show.
Experience a little luxury and feel like royalty with a four night stay at a fabulous five star hotel.


In the past, I've used Franz to be able to get all the messaging apps in one place. But… there's issues. Occasionally Franz decides to eat all the RAM. And there's no way to theme it to a dark interface.
It's no secret that Franz is an Electron based wrapper for web services. So I started wondering if an alternate browser (my main one is still Firefox) would be handy. Short form: Oh yes.
There's two reasons why I think using an alternate browser is better than using Franz.
First, I get to use Stylish so that I have have things tweaked how I want them. Vivaldi uses the Chrome web store, so it's trivial to install Stylish and install the Messenger Night, Dark Skype Web, Hangouts Dark Theme, and Hangouts: No Decorative Images themes. So I've got the dark styles taken care of, so my eyes don't bleed.
This is more than just a simple visual tweak – there's quite a bit of research that shows the default of "pure white background" is not good for our eyes (one site compares it to "staring at a lamp"), so being able to tweak and alter the color scheme is super important to me.
Then – and just as important – there's The Great Suspender (Chrome Web Store), or Suspend Tab (Firefox variant) which unloads tabs that aren't being used. That means that – unlike Franz – Vivaldi isn't eating up CPU cycles or memory for things I'm not looking at right then.
As someone who is regularly pushing my home computer to its limits, having that functionality is vitally important.
Yes, I would love it if the APIs were open again, so I could use Pidgin for everything like in the old days. But as much as I wish that would happen, even the hacks that allow hangouts and messenger in Pidgin (and I love you all for it) don't support the featureset that I need.
Best of all, because Vivaldi is a pretty straightforward browser, it's pretty easy for even non-techies to set up as a communication hub.