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Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe
The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then der...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131428/ https://www.ncbi.nlm.nih.gov/pubmed/25165442 http://dx.doi.org/10.3389/fncom.2014.00070 |
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author | Shlizerman, Eli Riffell, Jeffrey A. Kutz, J. Nathan |
author_facet | Shlizerman, Eli Riffell, Jeffrey A. Kutz, J. Nathan |
author_sort | Shlizerman, Eli |
collection | PubMed |
description | The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. |
format | Online Article Text |
id | pubmed-4131428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-41314282014-08-27 Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe Shlizerman, Eli Riffell, Jeffrey A. Kutz, J. Nathan Front Comput Neurosci Neuroscience The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. Frontiers Media S.A. 2014-08-13 /pmc/articles/PMC4131428/ /pubmed/25165442 http://dx.doi.org/10.3389/fncom.2014.00070 Text en Copyright © 2014 Shlizerman, Riffell and Kutz. 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 Shlizerman, Eli Riffell, Jeffrey A. Kutz, J. Nathan Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title | Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title_full | Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title_fullStr | Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title_full_unstemmed | Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title_short | Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
title_sort | data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131428/ https://www.ncbi.nlm.nih.gov/pubmed/25165442 http://dx.doi.org/10.3389/fncom.2014.00070 |
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