Classification of correlated patterns with a configurable analog vlsi neural network of spiking neurons and self-regulating plastic synapses

Authors:
Massimilian Giulioni;Mario Pannunzi;Davide Badoni;Vittorio Dante;Paolo Del Giudice
Affiliations:
-;-;-;-;-
Venue:
Neural Computation
Year:
2009

Citing 11
Cited 0

Analog VLSI and neural systems

Analog VLSI and neural systems
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VLSI analogs of neuronal visual processing: a synthesis of form and function
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Analog VLSI: Circuits and Principles
Spike-driven synaptic dynamics generating working memory states

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Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation

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Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

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A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory

IEEE Transactions on Neural Networks

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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.