We all know that our thoughts change from moment to moment. In other words, it is rare for our brain to dwell in the same state for more than a fraction of a second. Yet, because of technical limitations, much of cognitive neuroscience has arguably studied “snapshots” of brain activity. As a consequence, we have accumulated a wealth of knowledge about what the responses of brain areas or even individual neurons represent—for example, some neurons seem to respond selectively to faces—but we mostly lack an understanding of how these neural responses relate and contribute to the dynamic computations performed by large networks of neurons. In this project we attempt to address this shortcoming. Exploiting recent technical advances to record the activity of large numbers of neurons at once, we will move beyond the “snapshot” view of the brain to observe how the brain dynamically changes from one state to the next. Working in monkeys, we will characterize the dynamic responses of the prefrontal cortex, a brain area largely unique to primates that underlies our ability to flexibly choose among possible actions under ever-changing circumstances. The monkeys will be trained to associate a sensory input—such as a visual stimulus—with a motor output—such as a rapid eye movement to a target. The monkey will have to make different associations based on the context of the situation, the rules of which the monkey will have to learn. Our goal is to develop a model of the neural networks involved in so-called context-dependent computations, and compare this model’s predictions to the experimentally obtained data. We can also take advantage of sophisticated genetic techniques to perturb the activity of neurons in the prefrontal cortex, and compare the effects of those perturbations on the monkey’s actions to the effects of simulated perturbations on the computational model. This close marriage of theory and experiments will provide a deeper understanding of the function of the prefrontal cortex, establishing a conceptual framework to explain how cognition and behavior emerge from the dynamic behavior of neural networks.
Valerio Mante, University of Zurich
Bill Newsome, Stanford University