Abstract
Deep spiking neural networks are one of the promising eventbased sensor signal processing concepts. However, the practical application of such networks is difficult with standard deep neural network training packages. In this paper, we propose a vector-matrix description of a spike neural network that allows us to adapt the traditional backpropagation algorithm for signals represented as spike time sequences. We represent spike sequences as binary vectors. This enables us to derive expressions for the forward propagation of spikes and the corresponding spike training algorithm based on the back propagation of the loss function sensitivities. The capabilities of the proposed vector-matrix model are demonstrated on the problem of handwritten digit recognition on the MNIST data set. The classification accuracy on test data for spiking neural network with 3 hidden layers is equal to 98.14%.
Highlights
In recent years, conventional deep neural networks have been widely used in solving various artificial intelligence problems: image classification, object detection, speech recognition, natural language processing and much more [1]
Deep spiking neural networks (SNNs) are similar to conventional deep neural networks in terms of topology, but differ in neuron models
To study the possibilities of the proposed model, we modeled a multilayer SNN with three fully connected hidden layers
Summary
Conventional deep neural networks have been widely used in solving various artificial intelligence problems: image classification, object detection, speech recognition, natural language processing and much more [1]. Training such deep neural networks typically requires powerful GPUs and computing clusters. The application of conventional deep neural networks in areas with critical power consumption is problematic. An alternative are biologically inspired spiking neural networks (SNNs). Deep SNNs are similar to conventional deep neural networks in terms of topology, but differ in neuron models.
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