Lectures and seminars External MBB Seminar: Andreas Tolias “Inception Loops in Vision”

Abstract

Finding sensory stimuli that drive neurons optimally and mapping the response selectivity of neurons in general is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed inception loops, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in-silico nonlinear response modeling. Our end-to-end trained, deep learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy, and was used to synthesize optimal stimuli---Most Exciting Inputs (MEIs). For mouse V1, MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1.  When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.

Host

Jens Hjerling Leffler