Presentation 2005/11/12
Analog VLSI Implementation of a Resonate-and-Fire Neuron Model
Kazuki NAKADA, Tetsuya ASAI, Hatsuo HAYASHI,
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Abstract(in English) In this report, we propose analog VLSI implementation of a resonate-and-fire neuron (RFN) model. RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection and frequency preference. The RFN circuit was derived from the Lotka-Volterra model to mimic the subthreshold membrane dynamics of RFN model. Through SPICE simulations, we will show that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level. Furthermore, we will also show the noise effects of the performance of the signal detection in the RFN circuit. These results show that the RFN circuit are expected to be useful for large-scale integrated circuit implementation of silicon spiking neural networks.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Spiking neuron / resonate-and-fire neuron (RFN) model / analog VLSI implementation / coincidence detection / frequency preference
Paper # NC2005-69
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Committee NC
Conference Date 2005/11/12(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Analog VLSI Implementation of a Resonate-and-Fire Neuron Model
Sub Title (in English)
Keyword(1) Spiking neuron
Keyword(2) resonate-and-fire neuron (RFN) model
Keyword(3) analog VLSI implementation
Keyword(4) coincidence detection
Keyword(5) frequency preference
1st Author's Name Kazuki NAKADA
1st Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology()
2nd Author's Name Tetsuya ASAI
2nd Author's Affiliation Graduate School of Information Science and Technology
3rd Author's Name Hatsuo HAYASHI
3rd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
Date 2005/11/12
Paper # NC2005-69
Volume (vol) vol.105
Number (no) 419
Page pp.pp.-
#Pages 6
Date of Issue