[Seminar] Prof. Tatiana Engel: Flexible identification of cognitive computations from spikes
Behaviorally relevant signals are often represented in neural population dynamics, which evolve on a low-dimensional manifold embedded into a high-dimensional space of neural responses. Revealing population dynamics from spikes is challenging because the dynamics and embedding are nonlinear and obscured by diverse and noisy responses of individual neurons. We developed a flexible framework for inferring neural population dynamics, which learns the dynamics and embedding simultaneously from data avoiding ad hoc assumptions. We applied this framework to neural activity recorded from the primate cortex during decision making. We found that decision-related dynamics were inconsistent with simple hypotheses proposed previously and instead agreed with an attractor network mechanism.
Zoom link: https://oist.zoom.us/j/98260915981?pwd=dWNFRVQrcUhCNWhrbGhtYWs0TEZPUT09