Behold, the behaving brain!

In my opinion, THE most important shift in neuroscience over the past few years has been the focus on how behavior changes neural function across the whole brain. Even the sensory systems – supposedly passive passers-on of perfectly produced pictures of the world – will be shifted in unique ways by behavior. An animal walking will have different responses to visual stimuli than an animal that is just sitting around. Almost certainly, other behaviors will have other effects on the animal.

A pair of papers this week have made that point rather elegantly. First, Carsen Stringer and Marius Pachitariu from the Carandini/Harris labs have gobs of data from when they were recording ~10,000 neurons simultaneously. Marius Pachitariu has an excellent twitter thread explaining the work. I just want to take one particular point from this paper which is that you can explain a surprising amount of variance in the primary visual cortex – and all across the brain – simply by looking at the movement of the animal’s face.

In the figures below, they have taken movies of an animal’s face, extracted the motion energy (roughly, how much movement there is at that location in the video), and then used PCA to find the common ways that you can describe that movement. Using this kind of common motion, they then tried to predict the activity of individual neurons – while ignoring the traditional sensory or task information that you would normally be looking at.

The other paper is from Simon Musall and Matt Kaufman in Anne Churchland’s lab. He also has a nice twitter description of their work. Here, they used a technique that is able to image the whole brain simultaneously (though I am not sure to what depth), though at the cost of resolution (individual neurons are not identifiable but are averaged together). The animals are doing a task where they need to tell the difference between two tones, or two flashes of light. You can look for the brain areas involved in choice, or the areas involved in responding to vision or audio, and they are there (choice, kind of?).  But if you look at where movement is being represented it is everywhere.

The things that you would normally look for – the amount of brain activity you can explain by an animal’s decisions or its sensory responses – explain very little unique information.
This latter point is really important. If you had looked at the data and ignored the movement, you would have certainly found neurons that were correlated with decision-making. But once you take into account movement, that correlation drops away – the decisions are too correlated with general movement variables. People need to start thinking about how much of their neural data is responding to the task the animal is doing and how much is due to movement variables that are aligned to the task. This is really important! Simple averaging will not wash away this movement.

There is a lot more to both of these papers and both will be more than worth your time to dig into.

I’m not sure if you would have noticed this effect in either case if they weren’t recording from massive numbers of neurons simultaneously. This is a brave new world of neuroscience. How do we deal with this massively complex behavioral data at the same time that we deal with massive neural populations?

In my mind, the gold standard for how to analyze this data comes from Eva Naumann and James Fitzgerald in a paper out of the Engert lab. They are analyzing data from the whole brain of the zebrafish as it fictively swims around and responds to some moving background. Rather than throwing up their hands at the complexity of this data and the number of moving pieces what they did was very precisely quantify one particular aspect of the behavior. Then they followed the circuit step by step and tried to understand how the quantified behavior was transformed in the circuit. How did the visual stimuli guide the fish’s orientation in the water? What were the different ways the retina represented that visual information? How was this transformed by the relays into the brain? How was this information further transformed in the next step? How did the motor centers generate the different types of behavior that were quantified?

The brain evolved to produce behavior. In my opinion there is no way to understand the brain – any of it – if you don’t understand the behavior that the animal is producing.


Information theory of behavior

Biology can tell us what but theory tells us why. There is a new issue of Current Opinion in Neurobiology that focuses on the theory and computation in neuroscience. There’s tons of great stuff there, from learning and memory to the meaning of a spike to the structure of circuitry. I have an article in this issue and even made the cover illustration! It’s that tiny picture to the left; for some reason I can’t find a larger version but oh well…

Our article is “Information theory of adaptation in neurons, behavior, and mood“. Here’s how it starts:

Recently Stephen Hawking cautioned against efforts to contact aliens [1], such as by beaming songs into space, saying: “We only have to look at ourselves to see how intelligent life might develop into something we wouldn’t want to meet.” Although one might wonder why we should ascribe the characteristics of human behavior to aliens, it is plausible that the rules of behavior are not arbitrary but might be general enough to not depend on the underlying biological substrate. Specifically, recent theories posit that the rules of behavior should follow the same fundamental principle of acquiring information about the state of environment in order to make the best decisions based on partial data

Bam! Aliens. Anyway, it is an opinion piece where we try to push the idea that behavior can be seen as an information-maximization strategy. Many people have quite successfully pushed the idea that sensory neurons are trying to maximize their information about the environment so that they can represent it as well as possible. We suggest that maybe it makes sense to extend that up the hierarchy of biology. After all, people generally hate uncertainty, a low information environment, because it is hard to predict what is going to happen next.

Here is an unblocked copy of the article for those who don’t have access.


Sharpee, T., Calhoun, A., & Chalasani, S. (2014). Information theory of adaptation in neurons, behavior, and mood Current Opinion in Neurobiology, 25, 47-53 DOI: 10.1016/j.conb.2013.11.007

#cosyne14 day 3: Genes, behavior, and decisions

For other days (as they appear): 1, 2, 4deermouse - manisculatus

How do genes contribute to complex behavior?

Cosyne seems to have a fondness for inviting an ecogically-related researcher to remind us computational scientists that we’re actually studying animals that exist in, you know, an environment. Last year it was ants, this year deer mice.

Hopi Hoekstra gave an absolutely killer talk on a fairly complex behavior that is seen in deer mice: house building! Or rather, nest building. These mice will burrow to make a stereotyped nest with an entrance tunnel, a small nest, and an escape hatch that doesn’t quite make it to the surface (see below). But not every species of deer mouse builds their nest in precisely the same way. Only one (peromyscus) will build escape tunnels. Most will only make small little entrance tunnels (and possibly no nest?). Some don’t seem to dig at all. What causes this difference?

They crossed the species that makes long entrance tunnels and escape tunnels with a deer mouse nestrecently-diverged species (polionatus) that makes short entrance tunnels. These little guys will make tunnels that span the range from tiny to long, which suggests a multigenic trait. They did QTL on these crosses and found that only five genes are required for controlling nest building! One gene controls the construction of the escape tunnel, and four (three?) genes control the length of the entrance-tunnel length in an additive manner. One of the genes that is controlling tunnel length is an acetylcholine receptor in the basal ganglia (read: neuromodulator receptor in the ‘motivating’ part of the brain) that has been linked to addiction in other animals.

How many different behaviors do we have?

One of the themes that seemed to pop up this year was how to quantify animal behavior. It’s really not that obvious: is a reach for a coffee mug the same as a reach for my cell phone? Maybe, maybe not. Gordon Berman took their analytic tools to fly behavior in an attempt to map their ‘behavioral space’.

Screen shot 2014-03-02 at 8.09.40 AM

And okay, they were able to extract what look like unique behaviors: abdomen movements and wing movements and such. Okay, but that’s pretty hard for me to have an opinion on; what really sold me is when they decomposed a video of someone doing the hokey pokey. That gave them a hokey-pokey space which really corresponded to putting the left foot in, and also the left foot out, not to mention shaking things all about. It’s a shame that image is not up on the arXiv…

You know a talk is good when you start off incredibly skeptical and end up nodding along fervently by the end.

How do dopamine neurons signal prediction error?

Dopamine neurons are known to signal what is called ‘prediction error’: the difference between the expected reward and the received reward. How exactly are they doing it? Neir Eshel recorded from dopamine neurons (I missed where exactly) to expected and unexpected rewards. If you look at the reward vs. spike rate curve, they fit very well to a Hill function. In fact, every neuron they record from looks the same up to some multiplicative scaling factor. That’s a bit surprising to me because I thought there was much more heterogeneity in how, exactly, dopamine neurons respond to rewards…??

But they also find that the response to expected reward for any given neuron is the same Hill function as for the unexpected reward with some constant subtracted. They claim that this is beneficial because it allows even slowly responding neurons to contribute to prediction error without hitting the zero lower bound; I missed the logic of this when scribbling notes, though.

Gordon J. Berman, Daniel M. Choi, William Bialek, & Joshua W. Shaevitz (2013). Mapping the structure of drosophilid behavior arXiv arXiv: 1310.4249v1

Weber JN, Peterson BK, & Hoekstra HE (2013). Discrete genetic modules are responsible for complex burrow evolution in Peromyscus mice. Nature, 493 (7432), 402-5 PMID: 23325221
Photo from

Monday Open Thread: The Six Problems of Systems Neuroscience

I was brainstorming experiments and decided to make a list of what I think are the fundamental questions in systems neuroscience:

  1. Sensory: How do we represent the world?
  2. Motor: How do we create an action?
  3. Decision: How do we choose among competing alternatives?
  4. Learning: How do we remain plastic in changing environments?
  5. Computation: What are the underlying algorithms and computations?
  6. Modulation: How does internal state affect the nervous system?

Can anyone think of other broad questions in systems neuroscience? Should one of these not be here? Most other things I could think of belong here; for instance, “How do we deal with external and internal noise?” would probably be under Sensory or Learning. AYWNMBTTOF wrote a great post on what he considers the big questions of his field (taste) which I would subsume under Sensory.

I kind of hope this replaces the somewhat useless 23 Problems in Systems Neuroscience in terms of clarifying what we are studying.

You are what you eat – wait, no, you eat what you are. Wait, that’s not it…

Mouse Mansion

The public will never tire of the nature versus nurture debate but here’s a hint: the answer in biology is always both.  But if you’ve ever known any twins, you know they can have quite different personalities which, you would think, are attributable to differences in nurture of one sort or another.  To understand this better, some scientists did what scientists like to do which is trap some mice in a little mouse palace and watched how they behaved.  These mice were isogenic so there were no genetic differences (excepting, of course, what are probably trivial mutations and some hopefully minimal epigenetic influences).

Now the mouse palace is a wonderful place but there’s not really a lot to do there beyond roaming about, exploring their environment.  But not every mouse explores their environment in the same way: some mice like to explore the whole thing, some like to stay in just a few places where they are comfortable.  This alone suggests that the environment has a strong impact on behavior, over and above genetics.  But they also point to two other facts that they find: first, that over time the variance across the population in this exploratory difference increases.  Second, more neurons are born in the hippocampus, the area related to spatial maps and learning, in the animals that roam more than in the animals that stay put.

Now although this paper is pretty cool just for Mouse Mansion (it’s Big Brother: Mice!), there’s a lot to quibble with.  They never normalize the roaming variance by the roaming mean so we don’t really know that the variability is increasing.  We don’t know whether neurogenesis is increasing more in the animals that increase their roaming more.  And even if they did, it’s totally unsurprising that there would be more neurogenesis in the animals that explored more: because that’s just what we think neurogenesis is for!  Remembering more locations!  Further, from the first moment that they are recording from – 20 days (after birth ?) – the animals that explore the least continue to explore the least, and the animals that explore the most continue to explore the most, but everyone explores more as they get older.  So whatever induced most of the variability happened before the behavior was recorded.

We already know a lot about how exploratory behavior arises, and my guess is if you assayed the dopamine receptor expression level, you’d find the differences that you’re looking for to explain the behavior.  My naive guess as to what explains the difference is that it is mostly social – the authors don’t really demonstrate any effects of exploratory learning.  We know that mice have social structure, and social structure affects serotonin and dopamine levels which in turn affect exploratory behavior.  Now I don’t know if they looked at any type of social information in the Mouse Mansion, but I’d bet that the results of social play and social behavior prior to the start of the study are what creates the difference.  The fact that a few weeks of social play can change your behavior for the rest of your life?  Now that would be interesting.

But then, you don’t have to take my word for it.


Freund, J., Brandmaier, A., Lewejohann, L., Kirste, I., Kritzler, M., Kruger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of Individuality in Genetically Identical Mice Science, 340 (6133), 756-759 DOI: 10.1126/science.1235294

Oops! Late as always

I couldn’t get my act together to finish a paper write-up early in the week – holidays and then busy in lab, plus it’s about 4 papers (!) instead of the normal 1 – so enjoy these anecdotes from Information Processing:

WSJ: … When the great California Institute of Technology geneticist Seymour Benzer set out in the mid-1960s to find mutations in fruit flies that affected behavior, rather than mere anatomy, he was ridiculed for challenging the consensus that all behavior must be learned.

Benzer told the geneticist Max Delbrück about the plan to find behavioral mutants; Delbrück said it was impossible. To which Benzer replied: “But, Max, we found the gene, we’ve already done it!” (Benzer’s mother was more succinct: “From this, you can make a living?”) He was soon able to identify mutations related to hyperexcitability, learning, homosexuality and unusual circadian rhythms, like his own: Benzer was almost wholly nocturnal.

Since then, thanks to studies of human twins and a rash of genetic investigations in animals, it has become routinely accepted that most things, including personality, sexual orientation and intelligence, are to some degree affected by genes. The University of Virginia’s Eric Turkheimer has declared what he calls the “first law of behavior genetics”: that all human behavioral traits are heritable.

He’s got a lot of good stuff there, read it all!



Why you’ll become an alcoholic unless you get more sex

One very social behavior involves a man and a woman who love each other very much (hint: I’m talking about sex).  Flies who love each other very much obviously also mate, although you may not know that they undergo a courtship ritual first – not just any ol’ fly is getting to home plate.  That’s a behavior I’ll talk more about in a future post.  What I want to talk about here is instead what happens to that unlucky guy who, know matter how hard he tries, isn’t getting any.

A recent paper looked at this very question by taking a bunch of flies, and either having one group that either had a lot of sex or were rejected.  And can I say how awesome this sounds?  Listen to the protocol: one group of male flies experienced 1-hour sessions of sexual rejection three times a day for four days.  Another group experienced six-hour sessions of mating with multiple receptive virgin females for four days.  Let’s just say that you probably couldn’t do this kind of science in people.

The flies were then given the choice between food with alcohol and food without alcohol.  When the flies were sexually satisfied, they went without the alcohol; the flies were rejected needed that extra beer.  It turns out even virgin flies choose the alcohol – though they like it less than the rejected flies – which means that it is the lack of sex that mainly influences how much they need to drink.  If these same flies are allowed to mate?  Then they don’t need the alcohol anymore!

Desire, motivation and addiction in the brain are normally associated with the neural chemical dopamine.  But in this paper they looked at a neural peptide instead.  In humans, the neural peptide Y regulates alcohol consumption, as does all kinds of stress like PTSD and early maternal separation.  The equivalent peptide in fly is neural peptide F (NPF).  When they measured the amount of NPF in these flies, they found that it matched the desire for alcohol: the sexually rejected males had the lowest amount of NPF, the virgins had a little more, and the mated males had the most.  By decreasing the amount of NPF with siRNA or artificially activating it, they were able to control how much the flies wanted the alcohol.

So what is happening in the brain in response to sex?  Sex releases this neuropeptide – NPF in flies, NPY in humans – and the peptide is rewarding!  You love it (no surprise there)!  The peptide probably sets in motion changes in the larger reward system, modifying dopamine transmission over the course of many days.  This reveals the importance of investigating how we interact with our environment and fellow creatures in order to understand how our brain really works.


Shohat-Ophir, Kaun, Azanchi, Huberlein.  Sexual deprivation increases ethanol intake in Drosophila.  Science (335) 1351-1355.  DOI: 10.1126/science.1215932

See also the perspective.