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The statistical structure of noise in large neural populations

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If you have a question about this talk, please contact Guillaume Hennequin .

Sensory neurons represent stimulus information, but their responses can vary considerably across repeated stimulus presentations. This “response noise” has long figured in our models of computation in the brain, but we know little about how it is structured across large populations, or how it changes depending on which computations are being performed. I will present some recent work with structured latent variable models that attempts to advance on these questions. In particular, I will focus on data from large populations of macaque V4 neurons during a perceptual task, whose joint activity reveals the action of latent modulatory sources. The uncovered signals have pronounced anatomical and functional structure, their statistics depend on attentional state, and their values relate to past and future behaviour.

This talk is part of the Computational Neuroscience series.

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