Learning spatio-temporal V1 cells from diverse LGN inputs

Author:

Ruslim Marko A.,Burkitt Anthony N.ORCID,Lian YanboORCID

Abstract

AbstractSince the Nobel-winning discovery of simple cells and complex cells in cat’s primary visual cortex (V1) by Hubel and Wiesel, many experimental studies of the visual system of the brain have been conducted. Experimental data of V1 cells from animal recordings show spatio-temporal properties, namely that they display both spatial and temporal response properties. For spatial properties, each V1 cell responds to a specific feature (such as bars, blobs, etc.) in the visual space, which is called the receptive field of this cell. The receptive fields of different V1 cells are typically different in size, orientation, spatial frequencies, etc. Furthermore, V1 cells also display temporal properties, namely that the receptive fields of V1 cells can change over time, and there is a great variety of ways in which they change over time. For example, the population of V1 cells show a great diversity from monophasic response to biphasic response, and some V1 cells are selective to a preferred direction. However, given many computational learning models that explain how spatial properties of V1 cells can be learnt, how temporal properties emerge is still not well understood. In this paper, we use a simple learning model based on sparse coding to show that spatio-temporal V1 cells, such as biphasic and direction selective cell, can emerge via synaptic plasticity when diverse spatio-temporal LGN cells are used as upstream input to V1 cells. This work suggests that temporal along with spatial properties of V1 cells may simply come from a learning process that aims to encode upstream input with spatio-temporal properties, which greatly enhances our understanding of V1 cells.

Publisher

Cold Spring Harbor Laboratory

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