CIFASIS 20631
CENTRO INTERNACIONAL FRANCO ARGENTINO DE CIENCIAS DE LA INFORMACION Y DE SISTEMAS
Unidad Ejecutora - UE
artículos
Título:
Quantized State Simulation of Spiking Neural Networks
Autor/es:
GUILLERMO GRINBLAT; HERNÁN AHUMADA; ERNESTO KOFMAN
Revista:
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL
Editorial:
SAGE PUBLICATIONS LTD
Referencias:
Año: 2010
ISSN:
0037-5497
Resumen:
In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spikingneurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discretetime methods in use by the neuroscience community. We found that the computational costs of the QSS methods growalmost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. Weshow that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of thesystem that experience activity.
