Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractorine...

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Autores principales:	Richard Naud, Wulfram Gerstner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/646890b9f3ee4475b2294d325c2dc09a
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spelling	oai:doaj.org-article:646890b9f3ee4475b2294d325c2dc09a2021-11-18T05:52:49ZCoding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.1553-734X1553-735810.1371/journal.pcbi.1002711https://doaj.org/article/646890b9f3ee4475b2294d325c2dc09a2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23055914/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a 'quasi-renewal equation' which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.Richard NaudWulfram GerstnerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 10, p e1002711 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Richard Naud Wulfram Gerstner Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
description	The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation. As a modeling framework, we use the spike response model, also known as the generalized linear neuron model. Because of refractoriness, the effect of the most recent spike on the spiking probability a few milliseconds later is very strong. The influence of the last spike needs therefore to be described with high precision, while the rest of the neuronal spiking history merely introduces an average self-inhibition or adaptation that depends on the expected number of past spikes but not on the exact spike timings. Based on these insights, we derive a 'quasi-renewal equation' which is shown to yield an excellent description of the firing rate of adapting neurons. We explore the domain of validity of the quasi-renewal equation and compare it with other rate equations for populations of spiking neurons. The problem of decoding the stimulus from the population response (or PSTH) is addressed analogously. We find that for small levels of activity and weak adaptation, a simple accumulator of the past activity is sufficient to decode the original input, but when refractory effects become large decoding becomes a non-linear function of the past activity. The results presented here can be applied to the mean-field analysis of coupled neuron networks, but also to arbitrary point processes with negative self-interaction.
format	article
author	Richard Naud Wulfram Gerstner
author_facet	Richard Naud Wulfram Gerstner
author_sort	Richard Naud
title	Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
title_short	Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
title_full	Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
title_fullStr	Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
title_full_unstemmed	Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
title_sort	coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/646890b9f3ee4475b2294d325c2dc09a
work_keys_str_mv	AT richardnaud codinganddecodingwithadaptingneuronsapopulationapproachtotheperistimulustimehistogram AT wulframgerstner codinganddecodingwithadaptingneuronsapopulationapproachtotheperistimulustimehistogram
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Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

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