Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static one...

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Main Authors:	Sheik, Sadique, Coath, Martin, Indiveri, Giacomo, Denham, Susan L., Wennekers, Thomas, Chicca, Elisabetta
Format:	Online
Language:	English
Published:	Frontiers Research Foundation 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3272652/

id	pubmed-3272652
recordtype	oai_dc
spelling	pubmed-32726522012-02-15 Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System Sheik, Sadique Coath, Martin Indiveri, Giacomo Denham, Susan L. Wennekers, Thomas Chicca, Elisabetta Neuroscience Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. Frontiers Research Foundation 2012-02-06 /pmc/articles/PMC3272652/ /pubmed/22347163 http://dx.doi.org/10.3389/fnins.2012.00017 Text en Copyright © 2012 Sheik, Coath, Indiveri, Denham, Wennekers and Chicca. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sheik, Sadique Coath, Martin Indiveri, Giacomo Denham, Susan L. Wennekers, Thomas Chicca, Elisabetta
spellingShingle	Sheik, Sadique Coath, Martin Indiveri, Giacomo Denham, Susan L. Wennekers, Thomas Chicca, Elisabetta Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
author_facet	Sheik, Sadique Coath, Martin Indiveri, Giacomo Denham, Susan L. Wennekers, Thomas Chicca, Elisabetta
author_sort	Sheik, Sadique
title	Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
title_short	Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
title_full	Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
title_fullStr	Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
title_full_unstemmed	Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System
title_sort	emergent auditory feature tuning in a real-time neuromorphic vlsi system
description	Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.
publisher	Frontiers Research Foundation
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3272652/
_version_	1611503847296991232

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

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