Behavioral quantification: running is part of learning

One of the most accessible ways to study a nervous system is to understand how it generates behavior – its outputs. You can watch an animal and instantly get a sense of what it is doing and maybe even why it is doing it. Then you reach into the animal’s brain and try to find some connection that explains the what and the why.

Take the popular ‘eyeblink conditioning’ task that is used to study learning. You can puff a harmless bit of air at an animal and it will blink in response (wouldn’t you?). Like Pavlov’s dog, you can then pair it with another signal – a tone, a light, something like that – and the animal will slowly learn to associate the two. Eventually you just show the animal the other signal, flashing the light at them, and they will blink as if they were expecting an air puff coming. Simple enough but obviously not every animal is the same. There is a lot of variability in the behavior which could be due to any of a number of unexplored factors, from individual differences in experience to personality. If this is what we are using to investigate the underlying neuroscience, then, it places a fundamental limit on what we can know about the nervous system.

How can we neuroscientists overcome this? One very powerful technique has been to improve our behavioral quantification. I saw a fantastic example of this from Megan Carey when she visited Princeton earlier this year to talk about her work on cerebellum and learning. She had tons of interesting stuff but there was one figure she presented that simply blew me away.

First a bit of history is in order (apologies if I get some of this a bit wrong, my notes here are hazy). When experimenters first tried to get eyeblink conditioning to work with mice, they had trouble. Even though it seems like such a simple reflex the mice were performing very poorly on the task. Eventually, someone (?) found that allowing the mice to walk on a treadmill while experiencing the cues resulted in a huge increase in performance. Was this because they were unhappy being fixed in one place? Was it that they were unable to associate an puff of air to their eye with an environment when they were unable to manipulate their environment?

But there is still a lot of variability. Where does it come from? What you can now do is measure as much about the behavior as possible. Not just how much the animal blinks its eye, but how much it moves and how fast it moves, and how much it does all sorts of other stuff. And it turns out that if you measure the speed that the animal is walking there is a clear linear correlation with how long it takes the animal to learn.

Look at this figure – on the left, you can see how often each individual animal is responding to the air puff with an eyeblink (y-axis) as it is trained through time (x-axis). And on the right is how long it takes to reach some performance benchmark (y-axis) given the average speed the animal walks (x-axis).

So how do you test this? Make sure it is a causation not a meaningless correlation? Put them on a motorized treadmill and control the speed that they walk at. And BAM, most of the variability is gone! Look at the mess of lines in the behavior above and the clearly-delineated behavior below.

There’s a lesson here: when we study a ‘behavior’, there are a lot of other things that an animal is doing at the same time. We think they are irrelevant – we hope they are irrelevant – but often they are part of one bigger whole. If you want to study a behavior that an animal is performing, how else can you do it but by understanding as much about what the animal is doing as possible? How else but seeing how the motor output of the animal is linked together to become one complex form? Time and again, quantifying as many aspects of behavior as possible has revealed that it is in fact finely tuned but driven by some underlying variable that can be measured – once you figure out what it is.

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