Cargando…

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network

A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neu...

Descripción completa

Detalles Bibliográficos
Autores principales: Ukita, Jumpei, Yoshida, Takashi, Ohki, Kenichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405885/
https://www.ncbi.nlm.nih.gov/pubmed/30846783
http://dx.doi.org/10.1038/s41598-019-40535-4
_version_ 1783401179044118528
author Ukita, Jumpei
Yoshida, Takashi
Ohki, Kenichi
author_facet Ukita, Jumpei
Yoshida, Takashi
Ohki, Kenichi
author_sort Ukita, Jumpei
collection PubMed
description A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.
format Online
Article
Text
id pubmed-6405885
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-64058852019-03-12 Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network Ukita, Jumpei Yoshida, Takashi Ohki, Kenichi Sci Rep Article A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons. Nature Publishing Group UK 2019-03-07 /pmc/articles/PMC6405885/ /pubmed/30846783 http://dx.doi.org/10.1038/s41598-019-40535-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ukita, Jumpei
Yoshida, Takashi
Ohki, Kenichi
Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title_full Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title_fullStr Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title_full_unstemmed Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title_short Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
title_sort characterisation of nonlinear receptive fields of visual neurons by convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405885/
https://www.ncbi.nlm.nih.gov/pubmed/30846783
http://dx.doi.org/10.1038/s41598-019-40535-4
work_keys_str_mv AT ukitajumpei characterisationofnonlinearreceptivefieldsofvisualneuronsbyconvolutionalneuralnetwork
AT yoshidatakashi characterisationofnonlinearreceptivefieldsofvisualneuronsbyconvolutionalneuralnetwork
AT ohkikenichi characterisationofnonlinearreceptivefieldsofvisualneuronsbyconvolutionalneuralnetwork