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Electrical synapses for a pooling layer of the convolutional neural network in retinas
We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchica...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648117/ https://www.ncbi.nlm.nih.gov/pubmed/38026698 http://dx.doi.org/10.3389/fncel.2023.1281786 |
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author | Tsukamoto, Yoshihiko |
author_facet | Tsukamoto, Yoshihiko |
author_sort | Tsukamoto, Yoshihiko |
collection | PubMed |
description | We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry. |
format | Online Article Text |
id | pubmed-10648117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106481172023-01-01 Electrical synapses for a pooling layer of the convolutional neural network in retinas Tsukamoto, Yoshihiko Front Cell Neurosci Cellular Neuroscience We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry. Frontiers Media S.A. 2023-10-30 /pmc/articles/PMC10648117/ /pubmed/38026698 http://dx.doi.org/10.3389/fncel.2023.1281786 Text en Copyright © 2023 Tsukamoto. 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 | Cellular Neuroscience Tsukamoto, Yoshihiko Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title | Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title_full | Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title_fullStr | Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title_full_unstemmed | Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title_short | Electrical synapses for a pooling layer of the convolutional neural network in retinas |
title_sort | electrical synapses for a pooling layer of the convolutional neural network in retinas |
topic | Cellular Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648117/ https://www.ncbi.nlm.nih.gov/pubmed/38026698 http://dx.doi.org/10.3389/fncel.2023.1281786 |
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