A VLSI Spiking Feedback Neural Network with Negative Thresholding and Its Application to Associative Memory

• SASAKI Kan'ya

  Graduate School of Advanced Sciences of Matter, Hiroshima University

• MORIE Takashi

  Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology

• IWATA Atsushi

  Graduate School of Advanced Sciences of Matter, Hiroshima University

抄録

An integrate-and-fire-type spiking feedback network is discussed in this paper. In our spiking neuron model, analog information expressing processing results is given by the relative relation of spike firing. Therefore, for spiking feedback networks, all neurons should fire (pseudo-)periodically. However, an integrate-and-fire-type neuron generates no spike unless its internal potential exceeds the threshold. To solve this problem, we propose negative thresholding operation. In this paper, this operation is achieved by a global excitatory unit. This unit operates immediately after receiving the first spike input. We have designed a CMOS spiking feedback network VLSI circuit with the global excitatory unit for Hopfield-type associative memory. The circuit simulation results show that the network achieves correct association operation.

収録刊行物

• IEICE transactions on electronics

  IEICE transactions on electronics 89 (11), 1637-1644, 2006-11-01

  一般社団法人電子情報通信学会

キーワード

• spiking neuron model
• feedback network
• global excitatory unit
• negative thresholding
• associative memory

詳細情報

• CRID: 1572824502428556928
• NII論文ID: 110007538702
• NII書誌ID: AA10826283
• ISSN: 09168524
• 本文言語コード: en
• データソース種別:
  • CiNii Articles