A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)

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A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.
1 . Montes J, Gomez E, Merchán-Pérez A, Defelipe J, Peña JM (2013) A machine learning method for the prediction of receptor activation in the simulation of synapses. PLoS One 8:e68888 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Receptor(s): AMPA;
Transmitter(s): Glutamate;
Simulation Environment: Java; R;
Model Concept(s): Simplified Models;
Implementer(s): Montes, Jesus [jmontes at cesvima.upm.es];
Search NeuronDB for information about:  AMPA; Glutamate;
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