Publication
Title
Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations
Author
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.
Language
English
Source (journal)
Neural computation. - Cambridge, Mass.
Publication
Cambridge, Mass. : 2000
ISSN
0899-7667
Volume/pages
12:4(2000), p. 903-931
ISI
000086510400008
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 10.02.2011
Last edited 25.06.2017