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Learning to classify complex patterns using a VLSI network of spiking neurons


Mitra, S; Indiveri, G; Fusi, S (2008). Learning to classify complex patterns using a VLSI network of spiking neurons. In: Neural Information Processing Systems Foundation. Advances in neural information processing systems 20 : [proceedings of the 21th Conference on Advances in Neural Information Processing Systems (NIPS), Vancouver, British Columbia, Canada, on December 3 - 6, 2007]. Cambridge, Mass., US: MIT Press, 1009-1016.

Abstract

Real time classification of complex patterns of trains of spikes is a difficult and important computational problem. Here we propose a compact, low power, fully analog neuromorphic device which can learn to classify complex patterns of mean firing rates. The chip implements a network of integrate-and-fire neurons connected by bistable plastic synapses. Learning is supervised by a teacher which simply provides an extra input to the output neurons during training. The synapses are modified only as long as the current generated by the plastic synapses does not match the output desired by the teacher (as in the perceptron learning rule). Our device has been designed to be able to learn linearly separable patterns and we show in a series of tests that it can classify uncorrelated random spatial patterns of mean firing rates.

Abstract

Real time classification of complex patterns of trains of spikes is a difficult and important computational problem. Here we propose a compact, low power, fully analog neuromorphic device which can learn to classify complex patterns of mean firing rates. The chip implements a network of integrate-and-fire neurons connected by bistable plastic synapses. Learning is supervised by a teacher which simply provides an extra input to the output neurons during training. The synapses are modified only as long as the current generated by the plastic synapses does not match the output desired by the teacher (as in the perceptron learning rule). Our device has been designed to be able to learn linearly separable patterns and we show in a series of tests that it can classify uncorrelated random spatial patterns of mean firing rates.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:analog VLSI
Language:English
Date:2008
Deposited On:08 Mar 2009 20:13
Last Modified:05 Apr 2016 13:10
Publisher:MIT Press
Related URLs:http://books.nips.cc/ (Publisher)
http://nips.cc/Conferences/2007/ (Organisation)

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