Second order dimensionality reduction using minimum and maximum mutual information models.

Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To ov...

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Autores principales:	Jeffrey D Fitzgerald, Ryan J Rowekamp, Lawrence C Sincich, Tatyana O Sharpee
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/3d31e147728e467f9687e6bc3f6a6e5c
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spelling	oai:doaj.org-article:3d31e147728e467f9687e6bc3f6a6e5c2021-11-18T05:51:49ZSecond order dimensionality reduction using minimum and maximum mutual information models.1553-734X1553-735810.1371/journal.pcbi.1002249https://doaj.org/article/3d31e147728e467f9687e6bc3f6a6e5c2011-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22046122/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces.Jeffrey D FitzgeraldRyan J RowekampLawrence C SincichTatyana O SharpeePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 10, p e1002249 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jeffrey D Fitzgerald Ryan J Rowekamp Lawrence C Sincich Tatyana O Sharpee Second order dimensionality reduction using minimum and maximum mutual information models.
description	Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces.
format	article
author	Jeffrey D Fitzgerald Ryan J Rowekamp Lawrence C Sincich Tatyana O Sharpee
author_facet	Jeffrey D Fitzgerald Ryan J Rowekamp Lawrence C Sincich Tatyana O Sharpee
author_sort	Jeffrey D Fitzgerald
title	Second order dimensionality reduction using minimum and maximum mutual information models.
title_short	Second order dimensionality reduction using minimum and maximum mutual information models.
title_full	Second order dimensionality reduction using minimum and maximum mutual information models.
title_fullStr	Second order dimensionality reduction using minimum and maximum mutual information models.
title_full_unstemmed	Second order dimensionality reduction using minimum and maximum mutual information models.
title_sort	second order dimensionality reduction using minimum and maximum mutual information models.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/3d31e147728e467f9687e6bc3f6a6e5c
work_keys_str_mv	AT jeffreydfitzgerald secondorderdimensionalityreductionusingminimumandmaximummutualinformationmodels AT ryanjrowekamp secondorderdimensionalityreductionusingminimumandmaximummutualinformationmodels AT lawrencecsincich secondorderdimensionalityreductionusingminimumandmaximummutualinformationmodels AT tatyanaosharpee secondorderdimensionalityreductionusingminimumandmaximummutualinformationmodels
_version_	1718424719589376000

Second order dimensionality reduction using minimum and maximum mutual information models.

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