Strategies for the Optimization of Large Scale Networks of Integrate and Fire Neurons

Authors:
Manuel A. Sánchez-Montañés
Affiliations:
-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

Citing 4
Cited 0

Employing the Z-transform to optimize the calculation of the synaptic conductance of NMDA and other synaptic channels in network simulations

Neural Computation
Methods in Neuronal Modeling: From Ions to Networks

Methods in Neuronal Modeling: From Ions to Networks
Detailed Parallel Simulation of a Biological Neuronal Network

IEEE Computational Science & Engineering
Synthesis of Generalized Algorithms for the Fast Computation of Synaptic Conductances with Markov Kinetic Models in Large Network Simulations

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we summarize several techniques that allow a dramatic speed-up of the simulation of networks of integrate and fire (I & F) neurons. The speed-up these methods allow is investigated in simulations where the computation time is measured as a function of network activity. Several topics are discussed such as the current limits of real time (largest network that can be simulated in real time) and the computational convenience of mean-field approximations. We conclude that the simulation of large models of I & F neurons in real time is feasible within the current technology.