Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses.
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.
Giulioni
Massimilian
M
Italian National Institute of Health, Rome, Italy. massimiliano.giulioni@iss.infn.it
Pannunzi
Mario
M
Badoni
Davide
D
Dante
Vittorio
V
Del Giudice
Paolo
P
D020013:Calcium Signaling / Q000502:physiology; D003199:Computer Systems; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007858:Learning; D017774:Long-Term Potentiation; D008838:Microcomputers; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*