A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Massimiliano Giulioni, Mario Pannunzi, Davide Badoni, Vittorio Dante, Paolo Giudice


We summarize the implementation of an analog VLSI chip hosting a network of 32 integrate-and-fire (IF) neurons with spike-frequency adaptation and 2,048 Hebbian plastic bistable spike-driven stochastic synapses endowed with a self-regulating mechanism which stops unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and AER-based connectivity with external, AER compliant devices. We demonstrate the ability of the network to efficiently classify overlapping patterns, thanks to the self-regulating mechanism.