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The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex

This paper presents a model of rate coding in the cerebellar cortex. The pathway of input to output of the cerebellum forms an anatomically repeating, functionally modular network, whose basic wiring is preserved across vertebrate taxa. Each network is bisected centrally by a functionally defined ce...

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Autor principal: Gilbert, Mike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596575/
https://www.ncbi.nlm.nih.gov/pubmed/34902112
http://dx.doi.org/10.1007/s12311-021-01352-6
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author Gilbert, Mike
author_facet Gilbert, Mike
author_sort Gilbert, Mike
collection PubMed
description This paper presents a model of rate coding in the cerebellar cortex. The pathway of input to output of the cerebellum forms an anatomically repeating, functionally modular network, whose basic wiring is preserved across vertebrate taxa. Each network is bisected centrally by a functionally defined cell group, a microzone, which forms part of the cerebellar circuit. Input to a network may be from tens of thousands of concurrently active mossy fibres. The model claims to quantify the conversion of input rates into the code received by a microzone. Recoding on entry converts input rates into an internal code which is homogenised in the functional equivalent of an imaginary plane, occupied by the centrally positioned microzone. Homogenised means the code exists in any random sample of parallel fibre signals over a minimum number. The nature of the code and the regimented architecture of the cerebellar cortex mean that the threshold can be represented by space so that the threshold can be met by the physical dimensions of the Purkinje cell dendritic arbour and planar interneuron networks. As a result, the whole population of a microzone receives the same code. This is part of a mechanism which orchestrates functionally indivisible behaviour of the cerebellar circuit and is necessary for coordinated control of the output cells of the circuit. In this model, fine control of Purkinje cells is by input rates to the system and not by learning so that it is in conflict with the for-years-dominant supervised learning model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12311-021-01352-6.
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spelling pubmed-95965752022-10-27 The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex Gilbert, Mike Cerebellum Original Article This paper presents a model of rate coding in the cerebellar cortex. The pathway of input to output of the cerebellum forms an anatomically repeating, functionally modular network, whose basic wiring is preserved across vertebrate taxa. Each network is bisected centrally by a functionally defined cell group, a microzone, which forms part of the cerebellar circuit. Input to a network may be from tens of thousands of concurrently active mossy fibres. The model claims to quantify the conversion of input rates into the code received by a microzone. Recoding on entry converts input rates into an internal code which is homogenised in the functional equivalent of an imaginary plane, occupied by the centrally positioned microzone. Homogenised means the code exists in any random sample of parallel fibre signals over a minimum number. The nature of the code and the regimented architecture of the cerebellar cortex mean that the threshold can be represented by space so that the threshold can be met by the physical dimensions of the Purkinje cell dendritic arbour and planar interneuron networks. As a result, the whole population of a microzone receives the same code. This is part of a mechanism which orchestrates functionally indivisible behaviour of the cerebellar circuit and is necessary for coordinated control of the output cells of the circuit. In this model, fine control of Purkinje cells is by input rates to the system and not by learning so that it is in conflict with the for-years-dominant supervised learning model. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12311-021-01352-6. Springer US 2021-12-13 2022 /pmc/articles/PMC9596575/ /pubmed/34902112 http://dx.doi.org/10.1007/s12311-021-01352-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Gilbert, Mike
The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title_full The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title_fullStr The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title_full_unstemmed The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title_short The Shape of Data: a Theory of the Representation of Information in the Cerebellar Cortex
title_sort shape of data: a theory of the representation of information in the cerebellar cortex
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9596575/
https://www.ncbi.nlm.nih.gov/pubmed/34902112
http://dx.doi.org/10.1007/s12311-021-01352-6
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