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Configuring spiking neural network training algorithms
Spiking neural networks, based on biologically-plausible neurons with temporal information
coding, are provably more powerful than widely used artificial neural networks
based on sigmoid neurons (ANNs). However, training them is more challenging than
training ANNs. Several methods have been proposed in the literature, each with its
limitations: SpikeProp, NSEBP, ReSuMe, etc. And setting numerous parameters of
spiking networks to obtain good accuracy has been largely ad hoc.
In this work, we used automated algorithm configuration tools to determine optimal
combinations of parameters for ANNs, artificial neural networks with components
simulating glia cells (astrocytes), and for spiking neural networks with SpikeProp
learning algorithm. This allowed us to achieve better accuracy on standard datasets
(Iris and Wisconsin Breast Cancer), and showed that even after optimization augmenting
an artificial neural network with glia results in improved performance.
Guided by the experimental results, we have developed methods for determining
values of several parameters of spiking neural networks, in particular weight and output
ranges. These methods have been incorporated into a SpikeProp implementation
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