Self-Organization of Spatio-Temporal Visual Receptive Fields

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概要

• 論文の詳細を見る

• 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 then 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.

• 社団法人電子情報通信学会の論文
• 1996-07-25

著者

• TAKAHASHI Tomokazu

  Graduate School of Information Science, Nagoya University

• Takahashi Takashi

  Graduate School Of Information Science Nagoya University

• TAKAHASHI Takashi

  Doctoral Program in Engineering, University of Tsukuba

• HIRAI Yuzo

  Institute of Information Sciences and Electronics, University of Tsukuba

• Hirai Yuzo

  Institute Of Information Sciences And Electronics University Of Tsukuba

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