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Network architecture underlying maximal separation of neuronal representations
One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2013
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539730/ https://www.ncbi.nlm.nih.gov/pubmed/23316159 http://dx.doi.org/10.3389/fneng.2012.00019 |
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author | Jortner, Ron A. |
author_facet | Jortner, Ron A. |
author_sort | Jortner, Ron A. |
collection | PubMed |
description | One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon cells (KCs), and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a straightforward way to construct ecologically meaningful representations from this code. |
format | Online Article Text |
id | pubmed-3539730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-35397302013-01-11 Network architecture underlying maximal separation of neuronal representations Jortner, Ron A. Front Neuroeng Neuroscience One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon cells (KCs), and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a straightforward way to construct ecologically meaningful representations from this code. Frontiers Media S.A. 2013-01-03 /pmc/articles/PMC3539730/ /pubmed/23316159 http://dx.doi.org/10.3389/fneng.2012.00019 Text en Copyright © 2013 Jortner. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Neuroscience Jortner, Ron A. Network architecture underlying maximal separation of neuronal representations |
title | Network architecture underlying maximal separation of neuronal representations |
title_full | Network architecture underlying maximal separation of neuronal representations |
title_fullStr | Network architecture underlying maximal separation of neuronal representations |
title_full_unstemmed | Network architecture underlying maximal separation of neuronal representations |
title_short | Network architecture underlying maximal separation of neuronal representations |
title_sort | network architecture underlying maximal separation of neuronal representations |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3539730/ https://www.ncbi.nlm.nih.gov/pubmed/23316159 http://dx.doi.org/10.3389/fneng.2012.00019 |
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