A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences

Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at...

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Main Authors:	Mill, Robert, Coath, Martin, Wennekers, Thomas, Denham, Susan L.
Format:	Online
Language:	English
Published:	Public Library of Science 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158038/

id	pubmed-3158038
recordtype	oai_dc
spelling	pubmed-31580382011-08-29 A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences Mill, Robert Coath, Martin Wennekers, Thomas Denham, Susan L. Research Article Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response. Public Library of Science 2011-08-18 /pmc/articles/PMC3158038/ /pubmed/21876661 http://dx.doi.org/10.1371/journal.pcbi.1002117 Text en Mill et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly 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	Mill, Robert Coath, Martin Wennekers, Thomas Denham, Susan L.
spellingShingle	Mill, Robert Coath, Martin Wennekers, Thomas Denham, Susan L. A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
author_facet	Mill, Robert Coath, Martin Wennekers, Thomas Denham, Susan L.
author_sort	Mill, Robert
title	A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
title_short	A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
title_full	A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
title_fullStr	A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
title_full_unstemmed	A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
title_sort	neurocomputational model of stimulus-specific adaptation to oddball and markov sequences
description	Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response.
publisher	Public Library of Science
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158038/
_version_	1611471379369033728

A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences

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