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Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized t...

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Autores principales: Reimann, Michael W., Riihimäki, Henri, Smith, Jason P., Lazovskis, Jānis, Pokorny, Christoph, Levi, Ran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754339/
https://www.ncbi.nlm.nih.gov/pubmed/35020728
http://dx.doi.org/10.1371/journal.pone.0261702
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author Reimann, Michael W.
Riihimäki, Henri
Smith, Jason P.
Lazovskis, Jānis
Pokorny, Christoph
Levi, Ran
author_facet Reimann, Michael W.
Riihimäki, Henri
Smith, Jason P.
Lazovskis, Jānis
Pokorny, Christoph
Levi, Ran
author_sort Reimann, Michael W.
collection PubMed
description In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.
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spelling pubmed-87543392022-01-13 Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies Reimann, Michael W. Riihimäki, Henri Smith, Jason P. Lazovskis, Jānis Pokorny, Christoph Levi, Ran PLoS One Research Article In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit. Public Library of Science 2022-01-12 /pmc/articles/PMC8754339/ /pubmed/35020728 http://dx.doi.org/10.1371/journal.pone.0261702 Text en © 2022 Reimann et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Reimann, Michael W.
Riihimäki, Henri
Smith, Jason P.
Lazovskis, Jānis
Pokorny, Christoph
Levi, Ran
Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title_full Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title_fullStr Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title_full_unstemmed Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title_short Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
title_sort topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754339/
https://www.ncbi.nlm.nih.gov/pubmed/35020728
http://dx.doi.org/10.1371/journal.pone.0261702
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