Speaker
Description
Spiking neural networks which model action potentials in biological neurons are increasingly popular for machine learning applications thanks to ongoing progress in the hardware implementation of spiking networks in low-energy-consuming neuromorphic hardware. However, obtaining a spiking neural network model that solver a classification task as accurately as a formal neural network remains a challenge.
We study a spiking neural network model with non-trainable synaptic weights preset on base of logistic maps, similarly to what was proposed recently in the literature for formal neural networks. We show that one layer of spiking neurons with such weights can transform input vectors preserving the information about the classes of the input vectors, so that this information can be extracted from the neuron's output spiking rates by a subsequent classifier, such as Gradient Boosting.
The accuracy obtained on the Fisher's Iris classification task is 95%, with the deviation range of 5% over the five cross-validation folds.
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