Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations
Abstract
Markov kinetic models constitute a powerful framework to analyze patch-clamp 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.
Cite
Text
Giugliano. "Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations." Neural Computation, 2000. doi:10.1162/089976600300015646Markdown
[Giugliano. "Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations." Neural Computation, 2000.](https://mlanthology.org/neco/2000/giugliano2000neco-synthesis/) doi:10.1162/089976600300015646BibTeX
@article{giugliano2000neco-synthesis,
title = {{Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations}},
author = {Giugliano, Michele},
journal = {Neural Computation},
year = {2000},
pages = {903-931},
doi = {10.1162/089976600300015646},
volume = {12},
url = {https://mlanthology.org/neco/2000/giugliano2000neco-synthesis/}
}