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A subthreshold MOS neuron circuit based on the Volterra system
Asai, Tetsuya
Kanazawa, Yusuke
Amemiya, Yoshihito
??, ??
Location: http://hdl.handle.net/2115/5413
IEEE transactions on neural networks. 14(5), 2003, 1308-1312
http://dx.doi.org/10.1109/TNN.2003.816357

We present an analog neuron circuit consisting of a small number of metal-oxide semiconductor (MOS) devices operating in their subthreshold region. The dynamics of the circuit were designed to be equivalent to the well-known Volterra system to facilitate developing the circuit for a particular application. We show that a simple nonlinear transformation of system variables in the Volterra system enables designing a neuron-like oscillator, which can produce sequences in time of identically shaped pulses (spikes) by using current-mode subthreshold MOS circuits. We present experimental results of the fabricated neuron circuits as well as an application in an inhibitory neural network, where the neurons compete with each other in the frequency and time domains.

Belongs to: Hokkaido University Collection of Scholarly and Academic Papers

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A subthreshold MOS neuron circuit based on the Volterra system
Id. 5709496
Idioma inglés
Titulo A subthreshold MOS neuron circuit based on the Volterra system
Autor(es) Asai, Tetsuya
Kanazawa, Yusuke
Amemiya, Yoshihito
??, ??
Location http://hdl.handle.net/2115/5413
IEEE transactions on neural networks. 14(5), 2003, 1308-1312
http://dx.doi.org/10.1109/TNN.2003.816357
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Descripción We present an analog neuron circuit consisting of a small number of metal-oxide semiconductor (MOS) devices operating in their subthreshold region. The dynamics of the circuit were designed to be equivalent to the well-known Volterra system to facilitate developing the circuit for a particular application. We show that a simple nonlinear transformation of system variables in the Volterra system enables designing a neuron-like oscillator, which can produce sequences in time of identically shaped pulses (spikes) by using current-mode subthreshold MOS circuits. We present experimental results of the fabricated neuron circuits as well as an application in an inhibitory neural network, where the neurons compete with each other in the frequency and time domains.
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©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. IEEE, "IEEE Transactions on Neural Networks", 14-5, 2003, 1308-1312
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Fecha de contribución 25-oct-2007
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