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Spike-based learning with a generalized integrate and fire silicon neuron


Indiveri, G; Stefanini, F; Chicca, E (2010). Spike-based learning with a generalized integrate and fire silicon neuron. In: 2010 IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, FR, 30 May 2010 - 2 June 2010, 1951-1954.

Abstract

Spike-based learning circuits have been typically used in conjunction with linear integrate-and-fire neurons. As a new class of current-mode conductance-based silicon neurons has been recently developed, it is important to evaluate how the spike-based learning circuits perform, when interfaced to these new types of neuron circuits. Here, we describe a VLSI implementation of a current-mode conductance-based neuron, connected to synaptic circuits with spike-based learning capabilities. The conductance-based silicon neuron has built-in spike-frequency adaptation, refractory period mechanisms, and plasticity eligibility control circuits. The synaptic circuits exhibits realistic dynamics in the post-synaptic currents and comprise local spike-based learning circuits, controlled by the global post-synaptic eligibility circuits. We present experimental results which characterize the conductance-based neuron circuit properties and the spike-based learning circuits connected to it.

Abstract

Spike-based learning circuits have been typically used in conjunction with linear integrate-and-fire neurons. As a new class of current-mode conductance-based silicon neurons has been recently developed, it is important to evaluate how the spike-based learning circuits perform, when interfaced to these new types of neuron circuits. Here, we describe a VLSI implementation of a current-mode conductance-based neuron, connected to synaptic circuits with spike-based learning capabilities. The conductance-based silicon neuron has built-in spike-frequency adaptation, refractory period mechanisms, and plasticity eligibility control circuits. The synaptic circuits exhibits realistic dynamics in the post-synaptic currents and comprise local spike-based learning circuits, controlled by the global post-synaptic eligibility circuits. We present experimental results which characterize the conductance-based neuron circuit properties and the spike-based learning circuits connected to it.

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Additional indexing

Item Type:Conference or Workshop Item (Lecture), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:2 June 2010
Deposited On:12 Feb 2011 17:46
Last Modified:12 Aug 2017 01:21
Publisher:Institute of Electrical and Electronics Engineers Corporation (IEEE)
Series Name:Proceedings of ... IEEE International Symposium on Circuits and Systems (ISCAS)
Number:2010
ISBN:978-1-4244-5308-5
Publisher DOI:https://doi.org/10.1109/ISCAS.2010.5536980
Related URLs:http://www.iscas2010.org/ (Organisation)

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