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A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain
The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252271/ https://www.ncbi.nlm.nih.gov/pubmed/35795870 http://dx.doi.org/10.3389/fninf.2022.853098 |
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author | Lazar, Aurel A. Turkcan, Mehmet Kerem Zhou, Yiyin |
author_facet | Lazar, Aurel A. Turkcan, Mehmet Kerem Zhou, Yiyin |
author_sort | Lazar, Aurel A. |
collection | PubMed |
description | The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe. |
format | Online Article Text |
id | pubmed-9252271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92522712022-07-05 A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain Lazar, Aurel A. Turkcan, Mehmet Kerem Zhou, Yiyin Front Neuroinform Neuroscience The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe. Frontiers Media S.A. 2022-06-20 /pmc/articles/PMC9252271/ /pubmed/35795870 http://dx.doi.org/10.3389/fninf.2022.853098 Text en Copyright © 2022 Lazar, Turkcan and Zhou. https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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 Lazar, Aurel A. Turkcan, Mehmet Kerem Zhou, Yiyin A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title | A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title_full | A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title_fullStr | A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title_full_unstemmed | A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title_short | A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain |
title_sort | programmable ontology encompassing the functional logic of the drosophila brain |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252271/ https://www.ncbi.nlm.nih.gov/pubmed/35795870 http://dx.doi.org/10.3389/fninf.2022.853098 |
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