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Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories

Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the i...

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Detalles Bibliográficos
Autores principales: Cudone, Evan, Lower, Amelia M., McDougal, Robert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10597496/
https://www.ncbi.nlm.nih.gov/pubmed/37824576
http://dx.doi.org/10.1371/journal.pcbi.1011548
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author Cudone, Evan
Lower, Amelia M.
McDougal, Robert A.
author_facet Cudone, Evan
Lower, Amelia M.
McDougal, Robert A.
author_sort Cudone, Evan
collection PubMed
description Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both a point and morphologically-detailed cell model, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a moderate number of recent events alone. Knowledge of initial conditions or full synaptic input history is not required. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases as the number of known input events increases, to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.
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spelling pubmed-105974962023-10-25 Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories Cudone, Evan Lower, Amelia M. McDougal, Robert A. PLoS Comput Biol Research Article Biophysically detailed simulations of neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both a point and morphologically-detailed cell model, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a moderate number of recent events alone. Knowledge of initial conditions or full synaptic input history is not required. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases as the number of known input events increases, to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics. Public Library of Science 2023-10-12 /pmc/articles/PMC10597496/ /pubmed/37824576 http://dx.doi.org/10.1371/journal.pcbi.1011548 Text en © 2023 Cudone et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cudone, Evan
Lower, Amelia M.
McDougal, Robert A.
Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title_full Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title_fullStr Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title_full_unstemmed Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title_short Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
title_sort reproducibility of biophysical in silico neuron states and spikes from event-based partial histories
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10597496/
https://www.ncbi.nlm.nih.gov/pubmed/37824576
http://dx.doi.org/10.1371/journal.pcbi.1011548
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