Department of Neuroscience
UC Berkeley
Decoding decision-making dynamics
A challenge in studying the neural mechanisms underlying decision-making is that the process is subjective and unobservable. Techniques pioneered in the field of brain-machine interfaces, and first used to study motor control, offer a potential solution. Over the last few years, our work has applied these techniques to understand how prefrontal cortex enables optimal decision-making. Population-level decoding has allowed us to measure the dynamics of decision-making and examine how those dynamics are translated into action. Cortical oscillations co-ordinate firing rate dynamics between brain areas. Closed-loop control has enabled us to perform causal perturbations of the decision-making dynamics and demonstrate the necessity of orbitofrontal-hippocampal theta oscillations. Finally, I will describe a series of experiments that explore how information about behavioral context can be used to optimize learning and decision-making. Specifically, hippocampus constructs context-dependent task models in orbitofrontal cortex via a theta phase code. Taken together, our results show the complexity of the neural code that supports cognitive processes.