The Monnum spiking dataset for spike neural networks
Abstract
Spiking Neural Networks are a type of artificial neural network that mimics the way biological neural networks in the brain process information. Spiking neural networks form the foundation of the brain’s efficient information processing. While we don’t fully understand how these networks calculate, recent optimization techniques allow us to create increasingly complex functional spiking neural networks in a simulated environment. These methods promise to develop more efficient computing hardware and explore new possibilities in understanding brain circuit function. It is essential to have objective methods to compare their performance to speed up the development of such techniques. However, there are currently no widely accepted means of comparing the computational performance of spiking neural networks. We have introduced a new spike-based classification dataset that can be widely used to evaluate software performance and neuromorphic hardware implementations of spiking neural networks to address this issue. To achieve this, we have created a general procedure for converting audio signals into spiking neural network activity, drawing inspiration from neurophysiology. We created the Monnum digit dataset specifically for this study. Within the range of this research, We implemented a digit recognition system from 1 to 10 spoken in the Mongolian language for the Spike neural network. The last is data for training and testing, which was prepared in HDF5 format extension and then trained in the SNN network.
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