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Updated: Jun 20, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
Published on: April 15, 2015
Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and
Massimilian Giulioni1, Mario Pannunzi, Davide Badoni
1Italian National Institute of Health, Rome, Italy. massimiliano.giulioni@iss.infn.it
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.
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