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Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods se...

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Detalles Bibliográficos
Autores principales: Park, Mijung, Pillow, Jonathan W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3203052/
https://www.ncbi.nlm.nih.gov/pubmed/22046110
http://dx.doi.org/10.1371/journal.pcbi.1002219
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author Park, Mijung
Pillow, Jonathan W.
author_facet Park, Mijung
Pillow, Jonathan W.
author_sort Park, Mijung
collection PubMed
description The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets.
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spelling pubmed-32030522011-11-01 Receptive Field Inference with Localized Priors Park, Mijung Pillow, Jonathan W. PLoS Comput Biol Research Article The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets. Public Library of Science 2011-10-27 /pmc/articles/PMC3203052/ /pubmed/22046110 http://dx.doi.org/10.1371/journal.pcbi.1002219 Text en Park, Pillow. 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.
spellingShingle Research Article
Park, Mijung
Pillow, Jonathan W.
Receptive Field Inference with Localized Priors
title Receptive Field Inference with Localized Priors
title_full Receptive Field Inference with Localized Priors
title_fullStr Receptive Field Inference with Localized Priors
title_full_unstemmed Receptive Field Inference with Localized Priors
title_short Receptive Field Inference with Localized Priors
title_sort receptive field inference with localized priors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3203052/
https://www.ncbi.nlm.nih.gov/pubmed/22046110
http://dx.doi.org/10.1371/journal.pcbi.1002219
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