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- W2807144141 abstract "Spiking neural networks, based on biologically-plausible neurons with temporal informationcoding, are provably more powerful than widely used artificial neural networksbased on sigmoid neurons (ANNs). However, training them is more challenging thantraining ANNs. Several methods have been proposed in the literature, each with itslimitations: SpikeProp, NSEBP, ReSuMe, etc. And setting numerous parameters ofspiking networks to obtain good accuracy has been largely ad hoc.In this work, we used automated algorithm configuration tools to determine optimalcombinations of parameters for ANNs, artificial neural networks with componentssimulating glia cells (astrocytes), and for spiking neural networks with SpikeProplearning algorithm. This allowed us to achieve better accuracy on standard datasets(Iris and Wisconsin Breast Cancer), and showed that even after optimization augmentingan artificial neural network with glia results in improved performance.Guided by the experimental results, we have developed methods for determiningvalues of several parameters of spiking neural networks, in particular weight and outputranges. These methods have been incorporated into a SpikeProp implementation." @default.
- W2807144141 created "2018-06-13" @default.
- W2807144141 creator A5035524414 @default.
- W2807144141 date "2017-11-01" @default.
- W2807144141 modified "2023-09-23" @default.
- W2807144141 title "Configuring spiking neural network training algorithms" @default.
- W2807144141 hasPublicationYear "2017" @default.
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