Speaker
Description
We possess the ability to effortlessly and precisely time our actions in anticipation of events in the world. The seemingly effortless precision with which we execute most timing behaviors is remarkable given that information received from the world is often ambiguous and is corrupted by the influence of noise as it traverses through neural circuitry. Decades of research has shown that we are able to mitigate the effects of such uncertainty by relying on our prior experiences with such variables in the world. Bayesian theory provides a principled framework to study how trade-offs between prior knowledge and sensory uncertainty can shape perception, cognition, and motor function. Here we study this problem in the domain of timing to understand how low-dimensional geometries of neural population dynamics support Bayesian computations. In the first part of the talk, using results from electrophysiology and recurrent neural network modeling, I will discuss how cortical populations represent Bayesian behavior in monkeys during a timing task. Our results suggest that prior knowledge establishes curved manifolds of neural activity that warp underlying representations to generate Bayes-optimal estimates. Next, I will discuss how subcortical inputs interact with cortical dynamics to generate time intervals with an emphasis on the role of context-dependent input. Using in-vivo and in-silico approaches, we find that neural dynamics is temporally stretched or compressed to encode different time intervals. Finally, I will discuss how prior knowledge for temporal statistics could be acquired in a supervised fashion by cerebellar circuitry that is disynaptically connected to frontal cortical regions. Overall, these findings attempt to bridge insights from normative frameworks of Bayesian inference with potential neural implementations for the acquisition, estimation and production of optimal timing behavior.
