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A Spiking Neural Network Builder for Systematic Data-to-Model Workflow
In building biological neural network models, it is crucial to efficiently convert diverse anatomical and physiological data into parameters of neurons and synapses and to systematically estimate unknown parameters in reference to experimental observations. Web-based tools for systematic model build...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326306/ https://www.ncbi.nlm.nih.gov/pubmed/35909884 http://dx.doi.org/10.3389/fninf.2022.855765 |
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author | Gutierrez, Carlos Enrique Skibbe, Henrik Musset, Hugo Doya, Kenji |
author_facet | Gutierrez, Carlos Enrique Skibbe, Henrik Musset, Hugo Doya, Kenji |
author_sort | Gutierrez, Carlos Enrique |
collection | PubMed |
description | In building biological neural network models, it is crucial to efficiently convert diverse anatomical and physiological data into parameters of neurons and synapses and to systematically estimate unknown parameters in reference to experimental observations. Web-based tools for systematic model building can improve the transparency and reproducibility of computational models and can facilitate collaborative model building, validation, and evolution. Here, we present a framework to support collaborative data-driven development of spiking neural network (SNN) models based on the Entity-Relationship (ER) data description commonly used in large-scale business software development. We organize all data attributes, including species, brain regions, neuron types, projections, neuron models, and references as tables and relations within a database management system (DBMS) and provide GUI interfaces for data registration and visualization. This allows a robust “business-oriented” data representation that supports collaborative model building and traceability of source information for every detail of a model. We tested this data-to-model framework in cortical and striatal network models by successfully combining data from papers with existing neuron and synapse models and by generating NEST simulation codes for various network sizes. Our framework also helps to check data integrity and consistency and data comparisons across species. The framework enables the modeling of any region of the brain and is being deployed to support the integration of anatomical and physiological datasets from the brain/MINDS project for systematic SNN modeling of the marmoset brain. |
format | Online Article Text |
id | pubmed-9326306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93263062022-07-28 A Spiking Neural Network Builder for Systematic Data-to-Model Workflow Gutierrez, Carlos Enrique Skibbe, Henrik Musset, Hugo Doya, Kenji Front Neuroinform Neuroscience In building biological neural network models, it is crucial to efficiently convert diverse anatomical and physiological data into parameters of neurons and synapses and to systematically estimate unknown parameters in reference to experimental observations. Web-based tools for systematic model building can improve the transparency and reproducibility of computational models and can facilitate collaborative model building, validation, and evolution. Here, we present a framework to support collaborative data-driven development of spiking neural network (SNN) models based on the Entity-Relationship (ER) data description commonly used in large-scale business software development. We organize all data attributes, including species, brain regions, neuron types, projections, neuron models, and references as tables and relations within a database management system (DBMS) and provide GUI interfaces for data registration and visualization. This allows a robust “business-oriented” data representation that supports collaborative model building and traceability of source information for every detail of a model. We tested this data-to-model framework in cortical and striatal network models by successfully combining data from papers with existing neuron and synapse models and by generating NEST simulation codes for various network sizes. Our framework also helps to check data integrity and consistency and data comparisons across species. The framework enables the modeling of any region of the brain and is being deployed to support the integration of anatomical and physiological datasets from the brain/MINDS project for systematic SNN modeling of the marmoset brain. Frontiers Media S.A. 2022-07-13 /pmc/articles/PMC9326306/ /pubmed/35909884 http://dx.doi.org/10.3389/fninf.2022.855765 Text en Copyright © 2022 Gutierrez, Skibbe, Musset and Doya. 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 Gutierrez, Carlos Enrique Skibbe, Henrik Musset, Hugo Doya, Kenji A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title | A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title_full | A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title_fullStr | A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title_full_unstemmed | A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title_short | A Spiking Neural Network Builder for Systematic Data-to-Model Workflow |
title_sort | spiking neural network builder for systematic data-to-model workflow |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9326306/ https://www.ncbi.nlm.nih.gov/pubmed/35909884 http://dx.doi.org/10.3389/fninf.2022.855765 |
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