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 recently-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’.
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