Quantization-based simulation of spiking neurons: theoretical properties and performance analysis
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-thres...
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Argentina |
| Recursos: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/231433 |
| Acesso em linha: | http://hdl.handle.net/11336/231433 |
| Access Level: | acceso abierto |
| Palavra-chave: | DISCONTINUITY HANDLING EVENT-DRIVEN SIMULATION HYBRID SYSTEMS QUANTIZED STATE SYSTEMS SPIKING NEURAL NETWORKS https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Resumo: | 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. |
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