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A current-mode conductance-based silicon neuron for address-event neuromorphic systems


Livi, P; Indiveri, G (2009). A current-mode conductance-based silicon neuron for address-event neuromorphic systems. In: IEEE International Symposium on Circuits and Systems, 2009 (ISCAS 2009), Taipei, Taiwan, 24 May 2009 - 27 May 2009, 2898-2901.

Abstract

Silicon neuron circuits emulate the electrophysiological behavior of real neurons. Many circuits can be integrated on a single very large scale integration (VLSI) device, and form large networks of spiking neurons. Connectivity among neurons can be achieved by using time multiplexing and fast asynchronous digital circuits. As the basic characteristics of the silicon neurons are determined at design time, and cannot be changed after the chip is fabricated, it is crucial to implement a circuit which represents an accurate model of real neurons, but at the same time is compact, low-power and compatible with asynchronous logic. Here we present a current-mode conductance-based neuron circuit, with spike-frequency adaptation, refractory period, and bio-physically realistic dynamics which is compact, low-power and compatible with fast asynchronous digital circuits.

Silicon neuron circuits emulate the electrophysiological behavior of real neurons. Many circuits can be integrated on a single very large scale integration (VLSI) device, and form large networks of spiking neurons. Connectivity among neurons can be achieved by using time multiplexing and fast asynchronous digital circuits. As the basic characteristics of the silicon neurons are determined at design time, and cannot be changed after the chip is fabricated, it is crucial to implement a circuit which represents an accurate model of real neurons, but at the same time is compact, low-power and compatible with asynchronous logic. Here we present a current-mode conductance-based neuron circuit, with spike-frequency adaptation, refractory period, and bio-physically realistic dynamics which is compact, low-power and compatible with fast asynchronous digital circuits.

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20 citations in Web of Science®
25 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:neuromorphic
Language:English
Event End Date:27 May 2009
Deposited On:13 Mar 2010 09:41
Last Modified:05 Apr 2016 14:00
ISBN:978-1-4244-3827-3
Additional Information:© 2009 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.
Publisher DOI:10.1109/ISCAS.2009.5118408
Related URLs:http://conf.ncku.edu.tw/iscas2009/ (Organisation)
http://ieeexplore.ieee.org (Publisher)
http://www.ini.uzh.ch/node/21530
Permanent URL: http://doi.org/10.5167/uzh-32031

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