Model of MT and MST areas using an autoencoder

Katsuki Katayama, Masataka Ando and Tsuyoshi Horiguchi

Physica A: Statistical Mechanics and its Applications, 2003, vol. 322, issue C, 531-545

Abstract: We propose a model for a system with middle temporal neurons and medial superior temporal (MST) neurons by using a three-layered autoencoder. Noise effect is taken into account by using the framework of statistical physics. We define a cost function of the autoencoder, from which a learning rule is derived by a gradient descent method, within a mean-field approximation. We find a pair of values of two noise levels at which a minimum value of the cost function is attained. We investigate response properties of the MST neurons to optical flows for various types of motion at the pair of optimal values of two noise levels. We obtain that the response properties of the MST neurons are similar to those obtained from neurophysiological experiments.

Keywords: Neural network; MT neurons; MST neurons; Optical flow; Autoencoder; Learning; Mean-field approximation (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:322:y:2003:i:c:p:531-545

DOI: 10.1016/S0378-4371(02)01803-4

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