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Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis

Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters....

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Detalles Bibliográficos
Autores principales: Babaie Janvier, Tahereh, Robinson, Peter A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139319/
https://www.ncbi.nlm.nih.gov/pubmed/30250428
http://dx.doi.org/10.3389/fnhum.2018.00334
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author Babaie Janvier, Tahereh
Robinson, Peter A.
author_facet Babaie Janvier, Tahereh
Robinson, Peter A.
author_sort Babaie Janvier, Tahereh
collection PubMed
description Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters. In particular, these features assist in finding internal signals that represent input stimuli and their changes, which are exactly the types of quantities used in control systems to enable prediction of future input signals, and adjustment of gains which is argued to be the analog of attention in control theory. Corticothalamic dynamics are shown to be analogous to the classical proportional-integral-derivative (PID) filters that are widely used in engineering.
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spelling pubmed-61393192018-09-24 Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis Babaie Janvier, Tahereh Robinson, Peter A. Front Hum Neurosci Neuroscience Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters. In particular, these features assist in finding internal signals that represent input stimuli and their changes, which are exactly the types of quantities used in control systems to enable prediction of future input signals, and adjustment of gains which is argued to be the analog of attention in control theory. Corticothalamic dynamics are shown to be analogous to the classical proportional-integral-derivative (PID) filters that are widely used in engineering. Frontiers Media S.A. 2018-09-10 /pmc/articles/PMC6139319/ /pubmed/30250428 http://dx.doi.org/10.3389/fnhum.2018.00334 Text en Copyright © 2018 Babaie Janvier and Robinson. http://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
Babaie Janvier, Tahereh
Robinson, Peter A.
Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title_full Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title_fullStr Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title_full_unstemmed Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title_short Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis
title_sort neural field theory of corticothalamic prediction with control systems analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139319/
https://www.ncbi.nlm.nih.gov/pubmed/30250428
http://dx.doi.org/10.3389/fnhum.2018.00334
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