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Self-organization of spatio-temporal visual receptive fields[edit]

T. Takahashi and Y. Hirai,[edit]

IEICE Transactions on Information and Systems, vol.E79-D, no.7, pp.980--989, 1996.

Abstract[edit]

A self-organizing neural network model of spatio-temporal visual receptive fields is proposed. It consists of a one-layer linear learning network with multiple temporal input channels, and each temporal channel has different impulse response. Every weight of the learning network is modified according to a Hebb-type learning algorithm proposed by Sanger. It is shown by simulation studies that various types of spatio-temporal receptive fields are self-organized by the network with random noise inputs. Some of them have similar response characteristics to X- and Y-type cells found in mammalian retina. The properties of receptive fields obtained by the network are analyzed theoretically. It is shown that only circularly symmetric receptive fields change their spatio-temporal characteristics depending on the bias of inputs. In particular, when the inputs are non-zero mean, the temporal properties of center-surround type receptive fields become heterogeneous and alter depending on the positions in the receptive fields.

Keywords[edit]

self-organization,spatio-temporal receptive field

PDF[edit]

http://search.ieice.org/1996/files/e000d07.htm#e79-d,7,980


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