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Title
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Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Neural computation. - Cambridge, Mass.
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Publication
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Cambridge, Mass.
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2000
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ISSN
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0899-7667
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DOI
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10.1162/089976600300015646
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Volume/pages
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12
:4
(2000)
, p. 903-931
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ISI
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000086510400008
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Full text (Publisher's DOI)
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