A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to e...

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Autores principales:	Ana Calabrese, Joseph W Schumacher, David M Schneider, Liam Paninski, Sarah M N Woolley
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2e91944516274c26986f305780334fda
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spelling	oai:doaj.org-article:2e91944516274c26986f305780334fda2021-11-18T07:00:34ZA generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.1932-620310.1371/journal.pone.0016104https://doaj.org/article/2e91944516274c26986f305780334fda2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21264310/?tool=EBIhttps://doaj.org/toc/1932-6203In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.Ana CalabreseJoseph W SchumacherDavid M SchneiderLiam PaninskiSarah M N WoolleyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 1, p e16104 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ana Calabrese Joseph W Schumacher David M Schneider Liam Paninski Sarah M N Woolley A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
description	In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.
format	article
author	Ana Calabrese Joseph W Schumacher David M Schneider Liam Paninski Sarah M N Woolley
author_facet	Ana Calabrese Joseph W Schumacher David M Schneider Liam Paninski Sarah M N Woolley
author_sort	Ana Calabrese
title	A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
title_short	A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
title_full	A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
title_fullStr	A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
title_full_unstemmed	A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
title_sort	generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/2e91944516274c26986f305780334fda
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A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

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