Title
Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Cambridge, Mass. ,
Subject
Biology
Human medicine
Source (journal)
Neural computation. - Cambridge, Mass.
Volume/pages
12(2000) :4 , p. 903-931
ISSN
0899-7667
ISI
000086510400008
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Markov kinetic models constitute a powerful framework to analyze patchclamp data from single-channel recordings and model the dynamics of ion conductances and synaptic transmission between neurons. In particular, the accurate simulation of a large number of synaptic inputs in wide-scale network models may result in a computationally highly demanding process. We present a generalized consolidating algorithm to simulate efficiently a large number of synaptic inputs of the same kind (excitatory or inhibitory), converging on an isopotential compartment, independently modeling each synaptic current by a generic n -state Markov model characterized by piece-wise constant transition probabilities. We extend our findings to a class of simplified phenomenological descriptions of synaptic transmission that incorporate higher-order dynamics, such as short-term facilitation, depression, and synaptic plasticity.
E-info
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