Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI.

Frontiers in Neuroscience
Massimiliano GiulioniP Del Giudice

Abstract

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of "high" and "low"-firing activity. Depending on the overall excitability, transitions to the "high" state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the "high" state retains a "working memory" of a stimulus until well after its release. In the latter case, "high" states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated "corrupted" "high" states comprising neurons of both excitatory populations. Within a "basin of attraction," the network dynamics "corrects" such states and re-establishes the prototypical "high" s...Continue Reading

Citations

Jul 28, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Andrew S CassidyAndreas G Andreou
Sep 19, 2014·Frontiers in Neuroinformatics·Fabio StefaniniGiacomo Indiveri
Aug 10, 2018·PLoS Computational Biology·Jason S SherfeyNancy J Kopell
Oct 16, 2015·Scientific Reports·Massimiliano GiulioniPaolo Del Giudice
May 28, 2019·Frontiers in Neuroscience·Hanna KerenChristian G Mayr
Oct 22, 2020·IScience·Richard GeorgeChristian Mayr

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Methods Mentioned

BETA
chip
chips

Software Mentioned

SpiNNaker
SCANDLE
Neurogrid

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