Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0,...

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Autores principales:	Mehdi Adibi, James S McDonald, Colin W G Clifford, Ehsan Arabzadeh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/550a0b73d84546aa982c7b00d9bd87d0
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spelling	oai:doaj.org-article:550a0b73d84546aa982c7b00d9bd87d02021-11-18T05:53:15ZPopulation decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.1553-734X1553-735810.1371/journal.pcbi.1003415https://doaj.org/article/550a0b73d84546aa982c7b00d9bd87d02014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24391487/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.Mehdi AdibiJames S McDonaldColin W G CliffordEhsan ArabzadehPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 1, p e1003415 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Mehdi Adibi James S McDonald Colin W G Clifford Ehsan Arabzadeh Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
description	Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.
format	article
author	Mehdi Adibi James S McDonald Colin W G Clifford Ehsan Arabzadeh
author_facet	Mehdi Adibi James S McDonald Colin W G Clifford Ehsan Arabzadeh
author_sort	Mehdi Adibi
title	Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
title_short	Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
title_full	Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
title_fullStr	Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
title_full_unstemmed	Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
title_sort	population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/550a0b73d84546aa982c7b00d9bd87d0
work_keys_str_mv	AT mehdiadibi populationdecodinginratbarrelcortexoptimizingthelinearreadoutofcorrelatedpopulationresponses AT jamessmcdonald populationdecodinginratbarrelcortexoptimizingthelinearreadoutofcorrelatedpopulationresponses AT colinwgclifford populationdecodinginratbarrelcortexoptimizingthelinearreadoutofcorrelatedpopulationresponses AT ehsanarabzadeh populationdecodinginratbarrelcortexoptimizingthelinearreadoutofcorrelatedpopulationresponses
_version_	1718424665702006784

Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.

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