Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-17599
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, [et al.]. 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, 1009-1016.
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.
|Item Type:||Book Section, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Neuroinformatics|
|DDC:||570 Life sciences; biology|
|Uncontrolled Keywords:||analog VLSI|
|Deposited On:||08 Mar 2009 20:13|
|Last Modified:||09 Jul 2012 03:44|
|Related URLs:||http://books.nips.cc/ (Publisher)|
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