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A robust sound perception model suitable for neuromorphic implementation

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mu...

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Autores principales: Coath, Martin, Sheik, Sadique, Chicca, Elisabetta, Indiveri, Giacomo, Denham, Susan L., Wennekers, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894459/
https://www.ncbi.nlm.nih.gov/pubmed/24478621
http://dx.doi.org/10.3389/fnins.2013.00278
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author Coath, Martin
Sheik, Sadique
Chicca, Elisabetta
Indiveri, Giacomo
Denham, Susan L.
Wennekers, Thomas
author_facet Coath, Martin
Sheik, Sadique
Chicca, Elisabetta
Indiveri, Giacomo
Denham, Susan L.
Wennekers, Thomas
author_sort Coath, Martin
collection PubMed
description We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.
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spelling pubmed-38944592014-01-29 A robust sound perception model suitable for neuromorphic implementation Coath, Martin Sheik, Sadique Chicca, Elisabetta Indiveri, Giacomo Denham, Susan L. Wennekers, Thomas Front Neurosci Neuroscience We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds. Frontiers Media S.A. 2014-01-17 /pmc/articles/PMC3894459/ /pubmed/24478621 http://dx.doi.org/10.3389/fnins.2013.00278 Text en Copyright © 2014 Coath, Sheik, Chicca, Indiveri, Denham and Wennekers. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Coath, Martin
Sheik, Sadique
Chicca, Elisabetta
Indiveri, Giacomo
Denham, Susan L.
Wennekers, Thomas
A robust sound perception model suitable for neuromorphic implementation
title A robust sound perception model suitable for neuromorphic implementation
title_full A robust sound perception model suitable for neuromorphic implementation
title_fullStr A robust sound perception model suitable for neuromorphic implementation
title_full_unstemmed A robust sound perception model suitable for neuromorphic implementation
title_short A robust sound perception model suitable for neuromorphic implementation
title_sort robust sound perception model suitable for neuromorphic implementation
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3894459/
https://www.ncbi.nlm.nih.gov/pubmed/24478621
http://dx.doi.org/10.3389/fnins.2013.00278
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