A convolutional network model of the primate middle temporal area

Abstract

Convolutional neural networks have many parallels with the primate visual cortex, including deep structures with sparse retinotopic connections, and feature maps with increasing specificity and invariance along feedforward paths. The present study explores the possibility of specifically training convolutional networks to resemble the primate cortex more closely. In particular, in addition to supervised learning to minimize an output error function, a deep layer is directly trained to approximate primate electrophysiology data. This method is used to develop a model of the macaque monkey dorsal stream that estimates heading and speed from visual input.