Topic: GLM-HMM

Understanding how internally driven cognitive processes (COPs) give rise to behavior in different species is one of the key challenges in systems neuroscience. Traditionally, neuroscience has aimed to isolate individual COPs by simplifying sensory stimuli and behavioral readouts. Yet in natural behavior, several COPs can co-occur, often with overlapping neural representations. The dynamics of such superimposed COPs are yet to be understood, as well as the extent to which they generalize across species. To tackle this question, we developed a naturalistic Virtual Reality setup in which two spec

Classical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. In this talk, I aim to convince you that this common view is incorrect. I will show results from applying a latent variable framework, the “GLM-HMM”, to hundreds of thousands of trials of mouse choice data. Our analysis reveals that mice don’t lapse. Instead, mice switch back and forth between engaged and disengaged behavior within a single session, and each mode of behavior lasts tens to hundreds of trials.