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Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous stat...

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Autores principales: Becker, Logan A., Li, Baowang, Priebe, Nicholas J., Seidemann, Eyal, Taillefumier, Thibaud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153295/
https://www.ncbi.nlm.nih.gov/pubmed/37131877
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author Becker, Logan A.
Li, Baowang
Priebe, Nicholas J.
Seidemann, Eyal
Taillefumier, Thibaud
author_facet Becker, Logan A.
Li, Baowang
Priebe, Nicholas J.
Seidemann, Eyal
Taillefumier, Thibaud
author_sort Becker, Logan A.
collection PubMed
description The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance [Formula: see text]) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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spelling pubmed-101532952023-05-03 Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs Becker, Logan A. Li, Baowang Priebe, Nicholas J. Seidemann, Eyal Taillefumier, Thibaud ArXiv Article The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance [Formula: see text]) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state. Cornell University 2023-09-11 /pmc/articles/PMC10153295/ /pubmed/37131877 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Becker, Logan A.
Li, Baowang
Priebe, Nicholas J.
Seidemann, Eyal
Taillefumier, Thibaud
Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title_full Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title_fullStr Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title_full_unstemmed Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title_short Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
title_sort exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153295/
https://www.ncbi.nlm.nih.gov/pubmed/37131877
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