Name
Title | ||
|---|---|---|
Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm |
Abstract | ||
|---|---|---|
Spiking neural network learning is closely connected to the plasticity mechanism chosen. The aim of this work is to assess, for a network of Leaky Integrate-and-Fire neurons with Spike-Timing-Dependent Plasticity, the influence of STDP constants and input encoding parameters on the accuracy of solving the simple model task of Fisher’s Iris classification. Parameter adjustment is performed with a genetic algorithm (GA) on base of NeuroEvolution of Augmenting Topologies (NEAT). The results of the GA with different fitness functions are compared to randomly generating the network parameters. The use of GA is shown to be able to persistently select the network parameters that achieve the highest accuracy for the configuration considered. The network is shown to be more sensitive to its input encoding parameters (encoding rates), than to plasticity constants. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.11.111 | Procedia Computer Science |
Keywords | DocType | Volume |
synaptic plasticity,spiking neural network,genetic algorithm,computational neuroscience | Conference | 145 |
ISSN | Citations | PageRank |
1877-0509 | 0 | 0.34 |
References | Authors | |
3 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksandr Sboev | 1 | 0 | 0.68 |
| Aleksandr Sboev | 2 | 0 | 0.68 |
| Alexey Serenko | 3 | 0 | 0.34 |
| Roman Rybka | 4 | 0 | 0.34 |
| Danila Vlasov | 5 | 0 | 0.68 |
| Danila Vlasov | 6 | 0 | 0.68 |
| Andrey Filchenkov | 7 | 46 | 15.80 |