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N2A: a computational tool for modeling from neurons to algorithms
The exponential increase in available neural data has combined with the exponential growth in computing (“Moore's law”) to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique chall...
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/PMC3901007/ https://www.ncbi.nlm.nih.gov/pubmed/24478635 http://dx.doi.org/10.3389/fncir.2014.00001 |
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author | Rothganger, Fredrick Warrender, Christina E. Trumbo, Derek Aimone, James B. |
author_facet | Rothganger, Fredrick Warrender, Christina E. Trumbo, Derek Aimone, James B. |
author_sort | Rothganger, Fredrick |
collection | PubMed |
description | The exponential increase in available neural data has combined with the exponential growth in computing (“Moore's law”) to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation. |
format | Online Article Text |
id | pubmed-3901007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39010072014-01-29 N2A: a computational tool for modeling from neurons to algorithms Rothganger, Fredrick Warrender, Christina E. Trumbo, Derek Aimone, James B. Front Neural Circuits Neuroscience The exponential increase in available neural data has combined with the exponential growth in computing (“Moore's law”) to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation. Frontiers Media S.A. 2014-01-24 /pmc/articles/PMC3901007/ /pubmed/24478635 http://dx.doi.org/10.3389/fncir.2014.00001 Text en Copyright © 2014 Rothganger, Warrender, Trumbo and Aimone. 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 Rothganger, Fredrick Warrender, Christina E. Trumbo, Derek Aimone, James B. N2A: a computational tool for modeling from neurons to algorithms |
title | N2A: a computational tool for modeling from neurons to algorithms |
title_full | N2A: a computational tool for modeling from neurons to algorithms |
title_fullStr | N2A: a computational tool for modeling from neurons to algorithms |
title_full_unstemmed | N2A: a computational tool for modeling from neurons to algorithms |
title_short | N2A: a computational tool for modeling from neurons to algorithms |
title_sort | n2a: a computational tool for modeling from neurons to algorithms |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901007/ https://www.ncbi.nlm.nih.gov/pubmed/24478635 http://dx.doi.org/10.3389/fncir.2014.00001 |
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