Churchland lab

I recently attended a meeting as part of the Simons Collaboration on the Global Brain. A postdoc in my lab, Matt Kaufman, has an award from this group and so attended as well. The goal of the collaboration is to understand the internal neural signals that interact with sensory inputs and motor outputs to shape behavior.

It was a fantastic meeting. Blaise Aguera y Arcas (Google) talked about machine intelligence and how it has advanced dramatically in recent years, easily accomplishing tasks that seemed impossible half a decade ago. Andrew Leifer (Princeton) talked about a new microscopy system for large scale imaging in c-elegans. Marlene Cohen described a surprising observation she made that the increased firing rates seen during attention are accompanied by decreased correlations among neurons.
A common theme among all the presentations was the idea that understanding these internal states requires considering the activity of large neural populations. A number of analyses were put forth to achieve that. The ones that were most interesting to me are designed to compare neural population activity during different kinds on behavioral states. We began to do this in our 2014 paper (see figure 7), but have really only begun to scratch the surface. The talks and conversations at the meeting expanded our thinking about new analyses we can use to get at this question. For instance, as an animal goes form making a decision to committing to action, does the population activity simply re-scale, or does it occupy a fundamentally new space?