Characterizing the nonlinear structure of shared variability in cortical neuron populations using neural networks

BioRxiv : the Preprint Server for Biology
Matthew R WhitewayDaniel A Butts


Sensory neurons often have variable responses to repeated presentations of the same stimulus. Simultaneous recordings of neural populations demonstrate that such variability is often shared across many neurons, and thus cannot be simply averaged away. Understanding the effects of this shared variability on neural coding requires an understanding of what the common drivers of variability are, and how they are related to each other and to stimulus processing. Latent variable models offer an approach for characterizing the structure of this shared variability in neural recordings, though most previous modeling approaches have either been linear or had to make very restrictive assumptions about the nonlinear structure. Here we demonstrate the use of an autoencoder neural network as a general means to fit nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neu...Continue Reading

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