I am really behind on this, but there is a spreadsheet with videos of most (?) of the talks from the Evolution 2014 conference.
Here are the talks that are relevant to the interests of the blog:
I am really behind on this, but there is a spreadsheet with videos of most (?) of the talks from the Evolution 2014 conference.
Here are the talks that are relevant to the interests of the blog:
To sum up day 1: I forgot my phone charger and all my toiletries and managed to lose my notebook by the end of the first lecture…! But I brought my ski gear, so there’s that. Mental priorities. For other days (as they appear): 2, 3, 4
Tom Jessel gave the opening talk on motor control. The motor cortex must send a command, or motor program, down the spinal cord but this causes a latency problem. It takes too much time to go down the spinal cord and back to have an appropriate error signal sent back (in case something goes wrong.) To solve the problem, the motor system keeps a local internal copy (PN, left). A simple model from engineering says that if you disrupt this gating, you can no longer control the gain of the movement and will get oscillations. So when Jessel interferes with PN activity, a mouse that would normally reach directly for a pellet instead moves it’s paw up and down in a slow forward circle – oscillating! I think that he also implicated a signal that directly modifies presynaptic release through GABA in this behavior.
(Apologies if this is wrong, as I said, I lost my notebook and am relying on memory for this one.)
Azim E, Jiang J, Alstermark B, & Jessell TM (2014). Skilled reaching relies on a V2a propriospinal internal copy circuit. Nature PMID: 24487617
Ah, beautiful San Diego a land rich in history. If you are here for a conference, chances are that you are going to be spending most of your time downtown in the Gaslamp district. If this is your first trip to San Diego, have fun there, amble along the bay and don’t worry too much. Might I suggest going to Craft&Commerce for excellent cocktails? Or try to sneak in to the hidden Noble Experiment (reservation by text message required; hidden behind a fake wall). But if you’re like me, wanting to get out and explore the actual city – well, here are some things to do.
From a conference-goer’s perspective, there are several ‘rings’ of neighborhoods – roughly corresponding to distance or the time it will take to visit. This map actually sums it up well. I’m going to focus on places you can easily get to by bus, with a few notes on places you might want to go by car.
Let’s first talk about Uptown (Hillcrest/Balboa Park on the map). Originally a ring of streetcar suburbs, San Diego decided that sounded too nice and ripped up all the streetcar tracks but left a somewhat dense and walkable set of neighborhoods. At it’s center is Balboa Park. This is a good place to focus your time. Here are the neighborhoods, some suggestions, and bus lines.
Note: outside of Normal Heights, it’s relatively easy to walk between all of these neighborhoods once you’re in Uptown. Also, you’ll easily know which neighborhood you are in by the neighborhood arches. Obviously, I am focusing on food/bar suggestions; each neighborhood has much more to offer than that.
Balboa Park – At the center of all the neighborhoods is Balboa Park, a truly gigantic park. At the center of Balboa Park is a set of buildings originally built for the World’s Fair in the Spanish style. There is tons to do here: many museums, gardens, just general perambulating. And, of course, the world-famous Zoo. If you go to the zoo, make sure to buy a ticket for the Skyfari arial tram for great views of the park and the city. The park itself is quite large and hosts things like a velodrome and ping pong courts. Bus directions.
Hillcrest – The largest of the uptown neighborhoods, often the extended area is known as ‘Hillcrest’. Probably the center of the LGBT community in San Diego. Try Snooze for brunch or Hash House A Go Go if you like vast amounts of deliciously greasy food. Nunu’s is a classic San Diego lounge that feels right out of Anchorman. Banker’s Hill Restaurant is one of my favorite restaurants, though some people feel similarly about Cucina Urbana. Bus directions.
North Park – The hippest of the uptown neighborhoods, it is adjacent to Hillcrest. Fun to walk around. Hands down the best coffee in San Diego is at Coffee & Tea Collective, though Cafe Calabria is the establishment candidate here. Carnitas’ Snack Shack is filled with delicious pork snacks and seems to always have a line, for good reason. My favorite bars are 7 Grand (for whiskey) and Bar Pink. The whole neighborhood is filled to the brim with food. Tiger! Tiger! offers good food and a great selection of beer. Another favorite for a wide selection of great beer is Toronado. Bus directions.
University Heights – A slightly more upscale and smaller neighborhood. Try the Red Fox for a really classic San Diego bar and excellent people watching (don’t expect tons of great food, however…). Small Bar is similar to Tiger! Tiger! in its beer selection. It’s also small. Try Bahn Thai or Plumeria for food. El Zarape is also a classic. Bus directions.
South Park – Hamilton’s is one of the epicenters of the San Diego beer scene. Go there. Lucky for you, SfN in 2013 is on second saturday which if you can get there early means delicious free food and cheap beer. Eclipse Chocolate also has some amazing sipping chocolate flavors. Bus directions.
Normal Heights – A bit more out of the way and less upscale, Normal Heights is just starting to come into its own (ie: gentrify). Lestat’s is one of the best coffee houses in San Diego (though not the best coffee), though if you want the emphasis on the beans go across the street to Dark Horse. Blind Lady Alehouse has great beer and the best pizza in San Diego. Viva Pops has Mexican-style popsicles. Polite Provisions looks like a 19th-century pharmacy and has great cocktails. I haven’t been to Sycamore Den yet but hear it also has great cocktails. Bus directions.
Other things to do
Outside of these areas? If you have a car, try going to the Wildlife Safari – it’s better than the Zoo. Maybe also drive out to Cabrillo National Monument.
Ocean Beach is great. Many of the beach towns in San Diego – and Southern California in general – have lost a lot of their soul. The exceptions are the ones that are difficult to get to, like Ocean Beach. Full of hippies, beach bums and the best hamburgers in America (be prepared for hours-long lines).
If you’re in Mission Valley/Old Town/Hotel Circle, you are in a land of shopping malls and tourist traps; spend as little time here as possible. If you are stuck in a hotel here, well, you happen to be right next to one of my favorite bars: Albie’s Beef Inn. It’s…an interesting place!
You might be tempted (as an academic) to go up to UC San Diego in La Jolla/University City – Although UCSD is in “La Jolla”, it’s separate from the traditional La Jolla, in a neighborhood called “University City” or UTC. UTC is a mess, try to avoid it unless you need to visit the campus or one of the other institutes such as Salk. La Jolla is expensive but nice; try visiting the cove. For a relatively inexpensive bite to eat, go to El Pescador or Puesto. Otherwise try Whisknladle or Prepkitchen.
Finally, try Tijuana! But don’t blame me for anything that happens, but Tijuana is a pretty fun place with good museums and an exciting food scene (not open on Sundays, however).
Other things to know about San Diego
Famous Bands from San Diego: Pinback, Blink 182, Crocodiles, Stone Temple Pilots, Tom Waits, Hot Snakes, Rocket From the Crypt
Best Authors from San Diego: Dr. Seuss
Best Movies set in San Diego: While some might expect me to say Anchorman, the clear answer is either Jurassic Park 2 or Top Gun.
Best Indian – There’s not a lot, but go to Himalayan or Suharti Farsan Mart
Best Sushi – Sushi Ota is the traditional king (with a location by the same people near downtown at Hane Sushi), but the recently opened Sushi Tadokoro is better and does not yet have the prestige price added to the menu. You will want to get the uni if you go to one of these places as San Diego Bay uni is honestly some of the best that you’ll find.
Best Other Japanese – Okan is the gold standard, and for ramen try Ramen Yamadaya. We have been going through a tsukemen trend lately so maybe get some of that.
Best Mexican – Tacos El Gordo, Super Cocina, Las Cuatro Milpas, El Borrego
Best New American – Banker’s Hill Restaurant, Whisknladle or Prep Kitchen
Best Chinese – Nope.
Best Fish Sandwiches/Tacos – El Pescador in La Jolla or German Mariscos taco truck if you can find it
Best Thai – Easily: Sab E Lee
Best coffee[shop] – Hands down the best coffee in San Diego is Coffee & Tea Collective. Other than that, you’re going for the coffeeshop not the coffee. Try Lestat’s (in Normal Heights, not Universight Heights) or The Living Room in La Jolla (or Birdrock Cafe, but it’s been a long time since I’ve made it there).
Here are some other suggestions.
I like the beach and art galleries. Where should I go?
La Jolla is the ticket! Just be aware that it’s a bit of a ways off, so I hope you have a rental car. A cab will be fairly spendy ($40+ each way, maybe?) and the bus takes >1 hour. Ocean Beach and Mission Beach are closer, but sans art galleries.
Where should I get tacos/lunch?
If you’re willing to walk a bit, it’s 20 minutes to some of the best mexican food in San Diego: Las Cuatro Milpas. Other than that, I don’t go downtown so you might as well check yelp. Just be sure to leave 20-30 minutes before lunch starts to beat the rush.
Is there a local San Diego cuisine?
Beyond practically inventing the fish taco, a local staple, there is one other hallowed bit of food: the carne asada fries. Think of them as a Mexican-influenced take on poutine. Make sure to get these while in San Diego, just try not to be sober while you eat them.
I have just returned from the Reinforcement Learning and Decision Making (RLDM) conference and it was…quite the learning experience. As a neuroscientist, I tend to only read other scientific papers written by neuroscientists so it is easy to forget how big the Reinforcement Learning community really is. The talks were split pretty evenly between the neuroscience/psychology community and the machine learning/CS community, with a smattering of other people (including someone who works on forestry problems to find the optimal response to forest fires!). All in all, it was incredibly productive and I learned a ton about the machine learning side of things while meeting great people.
I think my favorite fact that I learned was from Tom Stafford, which is that there is a color called the ‘tritan line’ which is visible to visual cortex but not to certain other visual areas (specifically the superior colliculus). Just the idea that there is a color invisible to certain visual area is…bizarre and awesome. The paper he presented is discussed on his blog here.
There were a few standout talks.
Joe Kable gave a discussion of the infamous marshmallow task, where a young child is asked to not eat a marshmallow while the adult leaves the room for some indeterminate amount of time. It turns out that if the child believes the adult’s returning time is distributed in a Gaussian fashion then it makes sense to wait but if the returning time follows a heavy-tailed distribution then it makes sense to eat the marshmallow. This is because the predicted amount of time until the adult returns increases as time passes for a heavy-tailed function. And indeed, if you ask subjects to do a delay task they act as if the distribution of delay times are heavy-tailed. See his paper here.
Yin Li used monkeys to ask how an animal’s learning rate changes depending on the situation. There is no one optimal learning rate: it depends on the situation. If you are in an environment where you a tracking a target with little noise until sudden dramatic changes (small variance in between sudden changes in mean), then you want a high learning rate; you are not at risk of being overly responsive to the internal variability of the signal while it is stationary On the other hand, if there is a very noisy signal whose mean does not change much, then you want a low learning rate. When a monkey is given a task like this, it does about as well as a Bayesian-optimal model. I’m not sure which one he used, though I think this is a problem that has gotten attention in vision (see Wark et al and DeWeese & Zador). Anyway, when they try to fit a bog-standard Reinforcement Learning model it cannot fit the data. This riled up the CS people in the audience who suggested that something called “adaptive learning RL” could have fit the data, a technique I am not aware of? Although Li’s point was that the basic RL algorithm is insufficient to explain behavior, it also highlights the lack of crosstalk between the two RL kingdoms.
Michael Littman gave an absolutely fantastic talk asking how multiple agents should coordinate their behavior. If you use RL, one possibility is just to treat other agents as randomly moving objects…but “that’s a bit autistic”, as Littman put it. Instead, you can do something like minimax or maximin. Then you just need to find the Nash equilibrium! Unfortunately this doesn’t always converge to the correct answer, there can be multiple equilibria, and it requires access to the other agent’s value. Littman suggested that side payments can solve a lot of these problems (I think someone was paging Coase).
Finally, the amazing Alex Kacelnik gave a fascinating talk about parasitism in birds, particularly cuckoos. It turns out that when you take into account costs of eggs and such, it might actually be beneficial to the host to raise 1-2 parasite eggs; at least, it’s not straight forward that killing the parasites is the optimal decision. Anne Churchland asked whether neurons in the posterior parietal cortex of rats show mixed sensory and decision signals, and then showed that they are orthogonal on the level of the population. Paul Phillips gave a very lucid talk detailing the history of dopamine and TD learning. Tom Dietterich showed how reinforcement learning is being used by the government to make decisions for fire and invasive-species control. And Pieter Abbeel showed robots! See, for instance, the PR2 Willow Garage fetching beer (other videos):
Some final thoughts:
1. CS people are interested in convergence proofs, etc. But in the end, a lot of their talks were really just them acting as engineers trying to get things to work in the real world. That’s not that far from what psychologists and neuroscientists are doing: trying to figure out why things are working the way that they are.
2. In that spirit, someone in psych/neuro needs to take the leading-edge of what CS people are doing and apply it to human/animal models of decision-making. I’ve never heard of Adaptive Reinforcement Learning; what else is there?
3. At the outset, it would help if they could make it clear what are the open research questions for each field. At the end, maybe there could be some discussion on how to get the fields to collaborate more.
4. Invite some economists! They have this whole thing called Decision Theory… and would have a lot to contribute.
Besides the great stuff on decision-making, the other part of the main meeting I wanted to discuss were some of the Big Talks. This is the place where some of the Big Guns of neuroscience were just doing their thing, talking about neuroscience.
Bill Bialek gave a talk with the title, “Are we asking the right questions?” His first slide appeared with a large “No” and he declared that it wouldn’t be a particularly interesting talk if the answer was Yes, would it? Unfortunately, Yes was the answer I was hoping for! I had wanted him to give a deep, introspective talk about the questions we’re asking, the things that are right and wrong about them, and how we can ask better questions. I’ve actually been wondering the same thing lately with respect to Bialek’s work. Bill Bialek is a statistical physicist who applies the methods of stat mech to neural systems. He gets really interesting results about the properties of large neural systems and neural coding, but I’m not quite sure if the answers he gets are relevant to the particular questions I’m interested in asking For the record, Bialek thought that we should be thinking about predictive coding. That is, how neurons reflect not information about the environment but rather information that can predict what the environment will be.
Eve Marder studies the lobster stomatogastric ganglia (STG) which is the neural system that controls the stomach, basically. It’s a great setup and has yielded tons of interesting results but there wasn’t exactly tons that was new in the talk. Fortunately, Marder is an excellent lecturer and it was interesting throughout. The most interesting comment that she made is that they actually know the whole connectome of the STG and have the ability to record from neurons in the system for weeks at a time! And yet they still don’t know how it all works. Take that, connectomists.
Terry Sejnowski gave a talk in two parts. Strangely, the first part had little to do with his own research and was instead used as a thematic introduction to the second part of his talk. He spent his time explaining how a camera based on a simple model of the retina – spiking only when it saw a new edge moved across the field of view – was able to naturally accomplish things such as identifying objects that researchers in computer vision have spent decades trying to do, only somewhat successfully. And emulating the retina naturally endows it with other great features such as amazing temporal precision and extremely low energy usage. See? Neuroscience is useful.
This led him to the second part of his talk: the Brain Activity Map (BAM). He started off telling the story of how the NYT article came into being, and why it seemed so sketchy. Basically: when Obama mentioned brain research in his State of the Union, a member of NIH that knew about the project happened to tweet, “Obama supports the Brain Activity Map!” (or something similar). From this one little tweet, the reporter’s instincts kicked in – he’d certainly never heard of any “Brain Activity Map” – and, after calling around to his sources, got the scoop on BAM. Sejnowski was here to finally let the rest of us neuroscientists in on what was going on.
I think a lot of what he talked about has been released in the recent Science perspective, but he certainly was excited about having met Obama… He also said (I think) that the BAM proposal was on a list of ten big science projects, and it beat them out. The data that will be stored – the activity of thousands or even millions of cells simultaneously – will be enormous. Microsoft, Google, and Qualcomm were in on the meetings and apparently basically said, “let us deal with that.” Since the data size is so enormous, the idea will be to have “brain observatories” where the data will reside; the data will be open access and analyses will be done on computers at the ‘observatories’. That way, no one has to worry about downloading the data sets!
Of course, the thing on everyone’s mind is whether funding for BAM will take away funding from other basic neuroscience research. When that came up in questions, Eve Marder said that the NIH heads have been discussing it and they want to make sure that there are no reductions in R01s (the ‘basic’ grant given to researchers). Basically, this is just more money. If it ever passes (to quote Sejnowski: “the hope is that both Democrats and Republicans have brains”).
Again, the important point here is that Sejnowski was really, really excited and it was kind of adorable.
Ah, Tiny Movshon (as my iphone kept trying to autocorrect). This was by far my favorite talk of the meeting, mostly because Movshon basically trolled all the rodent vision people in the audience. He gave a great, contradictory 45 minute talk about how if you’re not doing primate vision, you’re wasting your time. Okay, he really said that the mouse visual system is too different from the primate one to be of any use. Because it’s too evolutionarily far away. Except the cat visual system is great! Even though the cat is even more evolutionarily distant. But whatever, his solution to the problem of not being able to do genetics in monkeys and needing a replacement for mice is – wait for it – the mouse lemur! Obviously. Here is your future cuddly neuroscience overlord:
Anyway, at the end of the talk it was like someone disturbed a bees’ nest. All the rodent vision people were visibly distressed; would it be mean to say that as a lonely invertebrate person it was a nice bit of schadenfreude?
I spent a week recently in Salt Lake City at the Cosyne (COmputational and SYstems NEuroscience); people had told me that it was their favorite conference, and now I understand why. Other attendees have put up their reactions, so I figure it’s about time I got off the couch and did the same.
Probably the biggest effect this meeting had on me is that I started using twitter a bit more seriously – follow me at @neuroecology – and participated in my first “tweet-up” (is that really a thing?). There are lots of great neuroscientists tweeting though there should be more!
For simplicity of organization, there will be three posts on Cosyne: one on a few decision-making talks, one on The Big Talks, and one on neural correlates of foraging.
On decision-making, the first (and longest) talk was by Carlos Brody. His talk was focused on the question of how we make decisions in noisy environments. In this case, rats had to sit around listening to two speakers emit clicks at random (poisson) intervals and decide which speaker, left or right, was clicking more. We typically think of the way animals make these types of decisions is as a ‘noisy integrator‘: each point of information – each click – is added up with some noise thrown in there because we’re imperfect, forgetful, and the environment (and our minds!) are full of noise. The decision is then made when enough information has been accumulated that the animal can be confident in going one direction or another.
One small problem with this is that there are a lot of models that are consistent with the behavioral data. How noisy is the internal mind? Is it noisy at all? How forgetful are we? That sort of thing. The Brody lab fit the data to many models and found that the one that most accurately describes the observed behavior is a slow accumulator that is leaky (ie a bit forgetful) but where the only noise is from the sensory input! Actually, I have in my notes that is ‘lossless’ but also that it is ‘leaky’ so I’m not sure which of the two is accurate, but the important detail is that once the information is in the brain it gets computed on perfectly and our integration is noiseless; all the noise in the system comes from the sensory world.
They also recorded from two areas in the rat brain, the posterior parietal cortex (PPC) and the frontal orienting fields (FOF). The PPC is an area analogous to LIP where neural activity looks like it is integrating information; you can even look at the neural activity in response to every click from a speaker and see the information (activity) go up and down! The rational expectation is that you’d need this information to make a decision, right? Well, when he goes and inactivates the region there is no effect on the behavior. The other region he records from is the FOF which is responsible orienting the head (say, in the direction of the right decision). The neural activity here looks like a binary signal of ‘turn left’ or ‘turn right’. Inactivating this area just prior to the decision interferes with the ability to make a proper decision so the information is certainly being used here, though only as an output. Where the information is being integrated and sent from, though, is not clear; it’s apparently not the PPC (and then maybe not LIP)!
The second good talk was from a member of Paul Glimcher’s lab, Kenway Louie. He was interested in the question of why we make worse decisions when given more choices. Although he wanted to talk about value, he used a visual task as a proxy and explainer. Let’s say you have two noisy options where you weren’t certain which option was better; if the options are noisy but very distinct, it is easy to decide which one you want. However, if they are noisy and closer together in value it becomes harder and harder to distinguish them both behaviorally and as a matter of signal detection.
But now let’s add in a third object. It also has some noisy value, but you only have to make a decision between the first two. Should be easy right? Let’s add in some neuroscience: in the brain, one common way to represent the world is ‘divisive normalization’. Basically, the firing of a neuron is normalized by the activity of the other neurons in the region. So now that we’ve added in the third option, the firing of the neurons representing the value of the other two objects goes down. My notes were unfortunately…not great… so this is where I get a bit confused, because what I remember thinking doesn’t make total sense on reflection. But anyway: this normalization interferes with the probability distributions of the two options making it more difficult to make the right choice, although it is nonlinear and the human behavior matches nicely (I think). The paper is in press so hopefully I can report on it soon…
Paul Schrater gave a talk that was a mix of decision-making and foraging. His first and main point was that many of the things that we refer to having value are in fact secondary sources for value; money only has value inasmuch as it can buy things that are first-order needs such as food or movies or such. However, the same amount of money cannot always buy the same amount of goods so value is really an inference problem, and he claims it can of course be explained through Bayesian inference.
His next important point is that we really need to think about decision-making as a process. We are in a location, in which we must perform actions which have values and must fulfill some need states which, of course, influence our choice of location. Thinking about the decision-process as this loop makes us realize we need to have an answer to the stopping problem or, how long should I stay in a location before I leave to another location? The answer in economics tends to come from the answer to the Secretary Problem (how many secretaries should I interview before I just hire one?) and the answer in ecology comes from Optimal Foraging; in fact, both of these rely on measuring the expected mean value of the next option and both of these are wrong. We can instead think of the whole question as a question of learning and get an answer by reinforcement learning. Then when we stop relies not just on the mean expected reward but also the variance and other higher-order statistics. And how do humans do when tested in these situations? They rely on not just mean but also variance! And they fit quite closely to the reinforcement learning approach.
He also talked about the distractor problem that Kenway Louie discussed, but my notes here don’t make much sense and I’m afraid I don’t remember what his answer was…