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.


Monday Open Question: How many types of neurons are there in the brain?

How many types of neurons are in the brain? Not just number, but classes that represent some fundamental unit of computation? I tweeted an article about this a couple days ago and (justly) got pilloried for saying it counted classes in the brain rather than in two cortical regions. So what is the answer for the whole brain?

Obviously the answer depends on the brain that you are talking about. In the nematode C. elegans, we know that every hermaphrodite has 302 neurons and every male has 381. I believe these specifically male neurons get pruned in the developmental process if the animal does not become a male. These neurons tend to come in symmetric pairs or quartets, one showing up on each side of the body, so the number of neural ‘classes’ is on the order of 118 – though there is evidence that some neurons can be slightly different between their left and right side (ASEL and ASER, for example). Fruit flies (Drosophila) also show sex-specific neurons, with the genes Fruitless and Doublesex controlling whether certain neurons are masculinized or feminized. So not only are there going to be different classes of neurons in males and females, we know that there are single (or, again, symmetric) neurons that control single behaviors. On the other hand, in the fruit fly retina there are definitely distinct classes of neurons that are tiled across the eye. This should frame our thinking about the number of neural classes – there are classes with large numbers of neurons where convolution is useful (repeating the same computation across some space, such as visual or auditory or even musculature space) but perhaps neural function becomes more specific and class-less once specific outputs are needed.

The fruit fly brain may seem a bit silly, why bother comparing it to us cortical mammals? But adult Drosophila have roughly the same number of neurons as larval zebrafish, a vertebrate animal with a cerebrum that is a popular organism to study in neuroscience. So do we think that the zebrafish has just as many pre-planned neurons as Drosophila? Or is its neural structure somewhat looser, more patterned? I don’t have an answer here but I think it is worth thinking about the similarities and differences in these organisms that have similar numbers of neurons but quite different environmental and developmental needs.

Let’s turn to mammals. The area with the most well-defined number of cell classes is probably the retina. I’m not sure of the up-to-date estimates for number of cell classes but the classic description has two classes (rods/cones) in the input layer of the retina which can be further split depending on the number of colors an animal can see – for instance, humans have S, M, and L cones roughly corresponding to blue, green, and red light. This review roughly estimates that further into the eye there are two types of horizontal cells (first layer), ~12 types of bipolar cells (second layer), ~30 types of amacrine cells (third layer). From other sources we think there is something on the order of ~30 types of retinal ganglion cells, the output from the eye into the brain. Interestingly, this is roughly the same number of defined classes that we think the fruit fly has! But again, there may be species specificity here; something on the order of 95%+ of the output layer of monkey retina is a single cell class. So the eye alone has at least 80 classes of neurons and quite probably more.

The olfactory bulb is probably more complex. In mice, at least, the number of olfactory glomuleri that exist is probably on the order of one or two thousand? Though I would expect that once past this layer the classification will look more like retina or cortex – on the order of tens of subtypes.


Now let’s think about the cortex. The paper that inspired this post tried to estimate the number of cell classes by using single-cell RNA-sequencing in mice to identify the transcripts that are present in different cells and then attempts to cluster them into distinct sub-classes. It should be clear up front that the number of clusters you identify (1) may not be categorical but could be continuous between types of neurons and (2) may be different than if you clustered with a different method or with different types of data – functional responses, for instance. The authors in this paper make clear that they certainly find cells that look ‘intermediate’ between their clusters so whatever categories we get may not be very firm. For instance, in the following figure the size of the circles represents the number of cells they identify in a particular cluster and the thickness of the line between two circles is how many cells are intermediate between two clusters.


Without getting into too many details, they find that in two distinct anatomical regions they find roughly 50 inhibitory neurons that are common in their transcript types between the regions suggesting that the types of inhibition may be a common, repeated pattern across the brain. However, the types of 50 excitatory neurons were essentially unique to each of the two regions..Chuck Stevens has an interesting paper where he attempts to find lower and upper bounds on the number of possible cell classes in cortex. Let’s say that we accept the tiling principle, that the same types of cells are repeated again and again in a motif:

This argument can be extended to the neocortex. Underneath 1 mm2 of most regions of the primate cortical surface are about 105 neurons — the striate cortex is an exception with twice the number — each of which covers say 0.05 mm2 with its dendritic arbor (assumed to be 0.25 mm in diameter). Twenty neurons with dendritic arbors of this size would be required to cover a square millimetre of cortex, so the upper limit on number of cell types, if each must tile the cortex, is 105/20 = 5000, or an average of 1000 per layer. Now assume that the cortex has 10 times more neurons of each type than required to cover the cortex, a redundancy factor of 10 as guessed above for hippocampus: we thus would have about 100 neuron types per layer. If we believe there are a dozen ganglion cell types, two dozen amacrine cell types, and four dozen different kinds of inhibitory neurons in the CA1 region of hippocampus, 100 cell types per layer of neucortex seems like a reasonable number – not good news for the micromodelers.

Let’s update this estimate; we think that there may be 25 excitatory cell types per region. I don’t actually know off-hand the percentage of mouse cortex that these two regions encompass (a motor region, ALM, and a visual region, VISp) but let’s say they are roughly 10% of the cortical area each (this could be grossly wrong so feel free to correct me). We then might believe that cortex has on the order of 25 * 10 ~ 250 excitatory cell classes and ~50 inhibitory cell classes. Does this feel right? 300 classes for all of cortex?

But the cortex is the minority of the number of cells in the brain – the majority is in a single structure, the cerebellum. I don’t know of an estimate of the number of neural classes but a structure that is known for its beautiful tiling neurons seems more likely to have a fair bit of structure in its number of cell classes. What would we estimate here? Something similar to a primary sensory area, with ~50-100 cell classes? Something more something less?

And what about other subcortical regions in the brain like hypothalamus that are more directly responsible for specific behavior? Should we expect many thousands of distinct subtypes for each of the behaviors or something more patterned?

Tell me where I’m wrong.