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Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhib...

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Autores principales: Chapochnikov, Nikolai M., Pehlevan, Cengiz, Chklovskii, Dmitri B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629579/
https://www.ncbi.nlm.nih.gov/pubmed/37428907
http://dx.doi.org/10.1073/pnas.2117484120
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author Chapochnikov, Nikolai M.
Pehlevan, Cengiz
Chklovskii, Dmitri B.
author_facet Chapochnikov, Nikolai M.
Pehlevan, Cengiz
Chklovskii, Dmitri B.
author_sort Chapochnikov, Nikolai M.
collection PubMed
description One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN–LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.
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spelling pubmed-106295792023-11-08 Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction Chapochnikov, Nikolai M. Pehlevan, Cengiz Chklovskii, Dmitri B. Proc Natl Acad Sci U S A Biological Sciences One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN [Formula: see text] LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN [Formula: see text] LN and LN–LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations. National Academy of Sciences 2023-07-10 2023-07-18 /pmc/articles/PMC10629579/ /pubmed/37428907 http://dx.doi.org/10.1073/pnas.2117484120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Chapochnikov, Nikolai M.
Pehlevan, Cengiz
Chklovskii, Dmitri B.
Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title_full Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title_fullStr Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title_full_unstemmed Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title_short Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
title_sort normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629579/
https://www.ncbi.nlm.nih.gov/pubmed/37428907
http://dx.doi.org/10.1073/pnas.2117484120
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