Spoken Digits Classification Using a Spiking Neural Network with Fixed Synaptic Weights

The paper evaluates the applicability of an approach based on the usage of a spiking neural network with synaptic weights fixed from a uniform random distribution to solving audio data classification problems. On the example of the Free Spoken Digits Dataset pronounceable digit classification problem using a linear classifier trained on the output frequencies of spiking neurons as a decoder, an average accuracy of 94% was obtained. This shows that the proposed spiking neural network performs such a transformation of the audio data that makes it linearly separable. Numerical experiments demonstrated the stability of the algorithm to the parameters of the spike layer, and it was shown that the constants of the threshold potential and the membrane leakage time can be both equal and different for different neurons.