Spatiotemporal receptive fields of direction selective cells self-organized by an Infomax-based learning model

Spatiotemporal receptive fields of direction selective cells, or cells whose responses are selective to direction of motion of visual stimuli, were investigated theoretically. A learning algorithm for a spatiotemporal receptive field of a model visual cell was derived according to an information maximization principle. It was assumed that the model visual cell receives input from nonlagged and lagged model LGN neurons. According to the algorithm, model cells were trained by using computer-generated moving images as training data. After the training, cells tuned to various directions of motion were generated. Also, generated synaptic weight patterns of the cells were similar to Gabor wavelets and receptive fields of simple cells in the visual cortex. Thus, they were orientation and spatial frequency selective as well as direction selective.