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Anisotropic connectivity implements motion-based prediction in a spiking neural network

Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their conne...

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Autores principales: Kaplan, Bernhard A., Lansner, Anders, Masson, Guillaume S., Perrinet, Laurent U.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775506/
https://www.ncbi.nlm.nih.gov/pubmed/24062680
http://dx.doi.org/10.3389/fncom.2013.00112
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author Kaplan, Bernhard A.
Lansner, Anders
Masson, Guillaume S.
Perrinet, Laurent U.
author_facet Kaplan, Bernhard A.
Lansner, Anders
Masson, Guillaume S.
Perrinet, Laurent U.
author_sort Kaplan, Bernhard A.
collection PubMed
description Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory.
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spelling pubmed-37755062013-09-23 Anisotropic connectivity implements motion-based prediction in a spiking neural network Kaplan, Bernhard A. Lansner, Anders Masson, Guillaume S. Perrinet, Laurent U. Front Comput Neurosci Neuroscience Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory. Frontiers Media S.A. 2013-09-17 /pmc/articles/PMC3775506/ /pubmed/24062680 http://dx.doi.org/10.3389/fncom.2013.00112 Text en Copyright © 2013 Kaplan, Lansner, Masson and Perrinet. http://creativecommons.org/licenses/by/3.0/ 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) or licensor 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
Kaplan, Bernhard A.
Lansner, Anders
Masson, Guillaume S.
Perrinet, Laurent U.
Anisotropic connectivity implements motion-based prediction in a spiking neural network
title Anisotropic connectivity implements motion-based prediction in a spiking neural network
title_full Anisotropic connectivity implements motion-based prediction in a spiking neural network
title_fullStr Anisotropic connectivity implements motion-based prediction in a spiking neural network
title_full_unstemmed Anisotropic connectivity implements motion-based prediction in a spiking neural network
title_short Anisotropic connectivity implements motion-based prediction in a spiking neural network
title_sort anisotropic connectivity implements motion-based prediction in a spiking neural network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775506/
https://www.ncbi.nlm.nih.gov/pubmed/24062680
http://dx.doi.org/10.3389/fncom.2013.00112
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