Inception in visual cortex

Finding optimal stimuli for neurons has long been central for understanding information processing in the brain. However, it is hard because the search space is high-dimensional and sensory information processing is fundamentally nonlinear. With our collaborators Fabian Sinz, Andreas Tolias, Jake Reimer and Xaq Pitkow, we developed Inception Loops: a closed-loop optimization method combining in vivo recordings and in silico nonlinear modeling to find Most Exciting Images (MEIs) we show back to the brain. MEIs drove cells better than control stimuli revealing fascinating properties of mouse V1 cells. MEIs had sharp corners, curved strokes and pointillist textures, deviating strikingly from the standard V1 Gabor model.

inception-loops.jpg

We tried to drive maximal responses, but this technique could be applied more widely to provide better control over other forms of inception (in the movie sense: implanting ideas / percepts / neural patterns) with sensory stimuli or even optogenetics. Check out our preprint on bioRxiv.

Reference

Walker EY, Sinz FH, Froudarakis E, Fahey PG, Muhammad T, Ecker AS, Cobos E, Reimer J, Pitkow X, Tolias AS (2018): Inception in visual cortex: in vivo-silico loops reveal most exciting images. bioRxiv.

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