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Synaptic Size Dynamics as an Effectively Stochastic Process

Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processe...

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Autores principales: Statman, Adiel, Kaufman, Maya, Minerbi, Amir, Ziv, Noam E., Brenner, Naama
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183425/
https://www.ncbi.nlm.nih.gov/pubmed/25275505
http://dx.doi.org/10.1371/journal.pcbi.1003846
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author Statman, Adiel
Kaufman, Maya
Minerbi, Amir
Ziv, Noam E.
Brenner, Naama
author_facet Statman, Adiel
Kaufman, Maya
Minerbi, Amir
Ziv, Noam E.
Brenner, Naama
author_sort Statman, Adiel
collection PubMed
description Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes.
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spelling pubmed-41834252014-10-07 Synaptic Size Dynamics as an Effectively Stochastic Process Statman, Adiel Kaufman, Maya Minerbi, Amir Ziv, Noam E. Brenner, Naama PLoS Comput Biol Research Article Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes. Public Library of Science 2014-10-02 /pmc/articles/PMC4183425/ /pubmed/25275505 http://dx.doi.org/10.1371/journal.pcbi.1003846 Text en © 2014 Statman et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Statman, Adiel
Kaufman, Maya
Minerbi, Amir
Ziv, Noam E.
Brenner, Naama
Synaptic Size Dynamics as an Effectively Stochastic Process
title Synaptic Size Dynamics as an Effectively Stochastic Process
title_full Synaptic Size Dynamics as an Effectively Stochastic Process
title_fullStr Synaptic Size Dynamics as an Effectively Stochastic Process
title_full_unstemmed Synaptic Size Dynamics as an Effectively Stochastic Process
title_short Synaptic Size Dynamics as an Effectively Stochastic Process
title_sort synaptic size dynamics as an effectively stochastic process
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183425/
https://www.ncbi.nlm.nih.gov/pubmed/25275505
http://dx.doi.org/10.1371/journal.pcbi.1003846
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