A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware

Abstract Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the cont...

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Autores principales:	Natacha Vanattou-Saïfoudine, Chao Han, Renate Krause, Eleni Vasilaki, Wolfger von der Behrens, Giacomo Indiveri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f97c6154e5434aa3a6f60c171b48b097
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spelling	oai:doaj.org-article:f97c6154e5434aa3a6f60c171b48b0972021-12-02T17:41:12ZA robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware10.1038/s41598-021-97217-32045-2322https://doaj.org/article/f97c6154e5434aa3a6f60c171b48b0972021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97217-3https://doaj.org/toc/2045-2322Abstract Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this “neuromorphic engineering” approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.Natacha Vanattou-SaïfoudineChao HanRenate KrauseEleni VasilakiWolfger von der BehrensGiacomo IndiveriNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Natacha Vanattou-Saïfoudine Chao Han Renate Krause Eleni Vasilaki Wolfger von der Behrens Giacomo Indiveri A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
description	Abstract Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this “neuromorphic engineering” approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.
format	article
author	Natacha Vanattou-Saïfoudine Chao Han Renate Krause Eleni Vasilaki Wolfger von der Behrens Giacomo Indiveri
author_facet	Natacha Vanattou-Saïfoudine Chao Han Renate Krause Eleni Vasilaki Wolfger von der Behrens Giacomo Indiveri
author_sort	Natacha Vanattou-Saïfoudine
title	A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
title_short	A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
title_full	A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
title_fullStr	A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
title_full_unstemmed	A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
title_sort	robust model of stimulus-specific adaptation validated on neuromorphic hardware
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f97c6154e5434aa3a6f60c171b48b097
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A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware

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