Cargando…
Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks
One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, whic...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843526/ https://www.ncbi.nlm.nih.gov/pubmed/33519390 http://dx.doi.org/10.3389/fnsys.2020.615129 |
_version_ | 1783644165869928448 |
---|---|
author | Bae, Hyojin Kim, Sang Jeong Kim, Chang-Eop |
author_facet | Bae, Hyojin Kim, Sang Jeong Kim, Chang-Eop |
author_sort | Bae, Hyojin |
collection | PubMed |
description | One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain. |
format | Online Article Text |
id | pubmed-7843526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78435262021-01-30 Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks Bae, Hyojin Kim, Sang Jeong Kim, Chang-Eop Front Syst Neurosci Neuroscience One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain. Frontiers Media S.A. 2021-01-15 /pmc/articles/PMC7843526/ /pubmed/33519390 http://dx.doi.org/10.3389/fnsys.2020.615129 Text en Copyright © 2021 Bae, Kim and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bae, Hyojin Kim, Sang Jeong Kim, Chang-Eop Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title | Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title_full | Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title_fullStr | Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title_full_unstemmed | Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title_short | Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks |
title_sort | lessons from deep neural networks for studying the coding principles of biological neural networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843526/ https://www.ncbi.nlm.nih.gov/pubmed/33519390 http://dx.doi.org/10.3389/fnsys.2020.615129 |
work_keys_str_mv | AT baehyojin lessonsfromdeepneuralnetworksforstudyingthecodingprinciplesofbiologicalneuralnetworks AT kimsangjeong lessonsfromdeepneuralnetworksforstudyingthecodingprinciplesofbiologicalneuralnetworks AT kimchangeop lessonsfromdeepneuralnetworksforstudyingthecodingprinciplesofbiologicalneuralnetworks |