Tatiana Engel, Assistant Professor, Cold Spring Harbor Laboratory
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.
Tatiana Engel is an assistant professor of neuroscience at the Cold Spring Harbor Laboratory. She received her M.Sc. in Physics from Lomonosov Moscow State University in Russia, and she received her Ph.D. in theoretical physics from the Humboldt University of Berlin, Germany in 2007. She completed her postdoctoral training in computational neuroscience at Yale University and Stanford University. In 2017, she joined the faculty at the Cold Spring Harbor Laboratory.
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