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

Authors: Mariana Bergonzia, Joaquín Fernández, Rodrigo Castro, Alexandre Muzye and Ernesto Kofman.

Abstract: 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
optimisation or specialisation, obtain accurate results with low computational costs even in
large networks with a high level of connectivity

More information: https://ri.conicet.gov.ar/bitstream/handle/11336/231433/CONICET_Digital_Nro.c5cb4ef5-4f1a-44ab-9694-544cf0bec20f_B.pdf?sequence=2