Journal
NEURAL COMPUTATION
Volume 21, Issue 11, Pages 3106-3129Publisher
MIT PRESS
DOI: 10.1162/neco.2009.08-07-599
Keywords
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Funding
- EU [IST2001-38099 ALAVLSI]
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We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.
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