A spiking neural network with fixed synaptic weights based on logistic maps for a classification task

Abstract

Spiking neural networks are increasingly popular for machine learning applications, thanks to ongoing progress in the hardware implementation of spiking networks in low-energy-consuming neuromorphic hardware. Still, obtaining a spiking neural network model that solves a classification task with the same level of accuracy as a artificial neural network remains a challenge. Of especial relevance is the development of spiking neural network models trained on base of local synaptic plasticity rules that can be implemented either in digital neuromorphic chips or in memristive devices. However, existing spiking networks with local learning all have, to our knowledge, one-layer topology, and no multi-layer ones have been proposed so far. As an initial step towards resolving this problem, we study the possibility of using a non-trainable layer of spiking neurons as an encoder layer within a prospective multi-layer spiking neural network, implying that the prospective subsequent layers could be trained on base of local plasticity. 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. This is on par with other existing methods for Fisher’s Iris classification with spiking neural networks, which shows the prospective possibility of using the proposed layer as an encoder within a multi-layer network.