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Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are ofte...

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Detalles Bibliográficos
Autores principales: Wu, Ziniu, Rockwell, Harold, Zhang, Yimeng, Tang, Shiming, Lee, Tai Sing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589190/
https://www.ncbi.nlm.nih.gov/pubmed/34695120
http://dx.doi.org/10.1371/journal.pcbi.1009528
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author Wu, Ziniu
Rockwell, Harold
Zhang, Yimeng
Tang, Shiming
Lee, Tai Sing
author_facet Wu, Ziniu
Rockwell, Harold
Zhang, Yimeng
Tang, Shiming
Lee, Tai Sing
author_sort Wu, Ziniu
collection PubMed
description System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.
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spelling pubmed-85891902021-11-13 Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons Wu, Ziniu Rockwell, Harold Zhang, Yimeng Tang, Shiming Lee, Tai Sing PLoS Comput Biol Research Article System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons. Public Library of Science 2021-10-25 /pmc/articles/PMC8589190/ /pubmed/34695120 http://dx.doi.org/10.1371/journal.pcbi.1009528 Text en © 2021 Wu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Ziniu
Rockwell, Harold
Zhang, Yimeng
Tang, Shiming
Lee, Tai Sing
Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title_full Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title_fullStr Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title_full_unstemmed Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title_short Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons
title_sort complexity and diversity in sparse code priors improve receptive field characterization of macaque v1 neurons
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589190/
https://www.ncbi.nlm.nih.gov/pubmed/34695120
http://dx.doi.org/10.1371/journal.pcbi.1009528
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