Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/85892
Share/Export:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Title

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

AuthorsMontes, Jesús; Gómez, Elena; Merchán-Pérez, Ángel CSIC ORCID; DeFelipe, Javier CSIC ORCID; Peña, J. M.
Issue Date2013
PublisherPublic Library of Science
CitationPLoS ONE 8 (2013)
AbstractChemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations. © 2013 Montes et al.
URIhttp://hdl.handle.net/10261/85892
DOI10.1371/journal.pone.0068888
Identifiersdoi: 10.1371/journal.pone.0068888
issn: 1932-6203
Appears in Collections:(IC) Artículos




Files in This Item:
File Description SizeFormat
PLoS ONE 8 (2013).pdf1,21 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work

PubMed Central
Citations

3
checked on May 27, 2022

SCOPUSTM   
Citations

4
checked on May 24, 2022

WEB OF SCIENCETM
Citations

4
checked on May 25, 2022

Page view(s)

296
checked on May 27, 2022

Download(s)

220
checked on May 27, 2022

Google ScholarTM

Check

Altmetric

Dimensions


Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.