Cargando…
Interpreting wide-band neural activity using convolutional neural networks
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. D...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328518/ https://www.ncbi.nlm.nih.gov/pubmed/34338632 http://dx.doi.org/10.7554/eLife.66551 |
_version_ | 1783732334175977472 |
---|---|
author | Frey, Markus Tanni, Sander Perrodin, Catherine O'Leary, Alice Nau, Matthias Kelly, Jack Banino, Andrea Bendor, Daniel Lefort, Julie Doeller, Christian F Barry, Caswell |
author_facet | Frey, Markus Tanni, Sander Perrodin, Catherine O'Leary, Alice Nau, Matthias Kelly, Jack Banino, Andrea Bendor, Daniel Lefort, Julie Doeller, Christian F Barry, Caswell |
author_sort | Frey, Markus |
collection | PubMed |
description | Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity. |
format | Online Article Text |
id | pubmed-8328518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-83285182021-08-04 Interpreting wide-band neural activity using convolutional neural networks Frey, Markus Tanni, Sander Perrodin, Catherine O'Leary, Alice Nau, Matthias Kelly, Jack Banino, Andrea Bendor, Daniel Lefort, Julie Doeller, Christian F Barry, Caswell eLife Neuroscience Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity. eLife Sciences Publications, Ltd 2021-08-02 /pmc/articles/PMC8328518/ /pubmed/34338632 http://dx.doi.org/10.7554/eLife.66551 Text en © 2021, Frey et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Frey, Markus Tanni, Sander Perrodin, Catherine O'Leary, Alice Nau, Matthias Kelly, Jack Banino, Andrea Bendor, Daniel Lefort, Julie Doeller, Christian F Barry, Caswell Interpreting wide-band neural activity using convolutional neural networks |
title | Interpreting wide-band neural activity using convolutional neural networks |
title_full | Interpreting wide-band neural activity using convolutional neural networks |
title_fullStr | Interpreting wide-band neural activity using convolutional neural networks |
title_full_unstemmed | Interpreting wide-band neural activity using convolutional neural networks |
title_short | Interpreting wide-band neural activity using convolutional neural networks |
title_sort | interpreting wide-band neural activity using convolutional neural networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8328518/ https://www.ncbi.nlm.nih.gov/pubmed/34338632 http://dx.doi.org/10.7554/eLife.66551 |
work_keys_str_mv | AT freymarkus interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT tannisander interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT perrodincatherine interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT olearyalice interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT naumatthias interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT kellyjack interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT baninoandrea interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT bendordaniel interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT lefortjulie interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT doellerchristianf interpretingwidebandneuralactivityusingconvolutionalneuralnetworks AT barrycaswell interpretingwidebandneuralactivityusingconvolutionalneuralnetworks |