Title: Unlocking single-trial dynamics in parietal cortex during decision-making

Abstract: Neural firing rates in the macaque lateral intraparietal

(LIP) cortex exhibit gradual "ramping" that is commonly believed to reflect the accumulation of sensory evidence during decision-making.

However, ramping that appears in trial-averaged responses does not necessarily indicate that the spike rate ramps on single trials; a ramping average rate could also arise from instantaneous steps that occur at different times on each trial. In this talk, I will describe an approach to this problem based on explicit statistical latent-dynamical models of spike trains. We analyzed LIP spike responses using spike train models with: (1) ramping "accumulation-to-bound" dynamics; and (2) discrete "stepping" or "switching" dynamics. Surprisingly, we found that three quarters of choice-selective neurons in LIP are better explained by a model with stepping dynamics. We show that the stepping model provides an accurate description of LIP spike trains, allows for accurate decoding of decisions, and reveals latent structure that is hidden by conventional stimulus-aligned analyses.

Bio:

Jonathan grew up in Phoenix, Arizona, and attended the University of Arizona in Tucson as a Flinn Scholar, where he majored in mathematics and philosophy. After a year as a U.S. Fulbright fellow in Morocco studying North African literature, he attended graduate school at New York University, and received a Ph.D. in neuroscience in 2005 for research on statistical models of information processing in the early visual pathway.  He moved to London for a 3-year postdoctoral fellowship at the Gatsby Computational Neuroscience Unit of University College London, and in 2009, became an assistant professor at the University of Texas at Austin in the department of Psychology, Neuroscience, and Statistics & Data Science.  In 2014, Jonathan moved to Princeton as an assistant professor in the Princeton Neuroscience Institute and Psychology department.  Jonathan's current research sits at the border between neuroscience and statistical machine learning, focusing on computational and statistical methods for understanding how large populations of neurons transmit and process information.