title: Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses.
abstract: 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.
authors.affiliation: Italian National Institute of Health, Rome, Italy. massimiliano.giulioni@iss.infn.it
authors.forename: Massimilian
authors.initials: M
authors.lastname: Giulioni
authors.forename: Mario
authors.initials: M
authors.lastname: Pannunzi
authors.forename: Davide
authors.initials: D
authors.lastname: Badoni
authors.forename: Vittorio
authors.initials: V
authors.lastname: Dante
authors.forename: Paolo
authors.initials: P
authors.lastname: Del Giudice
mesh_terms: 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*