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Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations

N.B.T Neural and Bioelectronic Technologies Group, Department of Biophysical and Electronic Engineering, University of Genova, Genova, Italy

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.

This article has been cited by other articles:

				M. Rudolph and A. Destexhe Analytical Integrate-and-Fire Neuron Models with Conductance-Based Dynamics for Event-Driven Simulation Strategies. Neural Comput., September 1, 2006; 18(9): 2146 - 2210. [Abstract] [Full Text] [PDF]