Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.

ABSTRACT: A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated ce

SUBMITTER: Ukita J 

PROVIDER: S-EPMC6405885 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature