Quantized State Simulation of Spiking Neural Networks

GUILLERMO L. GRINBLAT; HERNÁN AHUMADA; ERNESTO KOFMAN

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL

SAGE PUBLICATIONS LTD

Año: 2012 vol. 88 p. 299 - 313

0037-5497

In this work, we explore the usage of Quantized State System (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete--event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS--methods grows almost linearly with the size of the network, while it grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system that experience activity.