Quantization-based simulation of spiking neurons: theoretical properties and performance analysis - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Simulation Année : 2023

Quantization-based simulation of spiking neurons: theoretical properties and performance analysis

Résumé

In this work we present an exhaustive analysis of the use of Quantized State Systems (QSS) algorithms for the discrete event simulation of Leaky Integrate and Fire models of spiking neurons. Making use of some properties of these algorithms, we first derive theoretical error bounds for the sub-threshold dynamics as well as estimates of the computational costs as a function of the accuracy settings. Then, we corroborate those results on different simulation experiments, where we also study how these algorithms scale with the size of the network and its connectivity. The results obtained show that the QSS algorithms, without any type of optimization or specialization, obtain accurate results with low computational costs even in large networks with a high level of connectivity.
Fichier principal
Vignette du fichier
Quantization_Based_Simulation_of_Spiking_Neurons-1.pdf (382.59 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04301560 , version 1 (23-11-2023)

Identifiants

Citer

Mariana Bergonzi, Joaquín Fernández, Rodrigo Castro, Alexandre Muzy, Ernesto Kofman. Quantization-based simulation of spiking neurons: theoretical properties and performance analysis. Journal of Simulation, inPress, pp.1-24. ⟨10.1080/17477778.2023.2284143⟩. ⟨hal-04301560⟩
11 Consultations
30 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More