Impact of increasing number of neurons on performance of neuromorphic architecture

Pattern recognition is used to classify the input data into different classes based on extracted key features. Increasing the recognition rate of pattern recognition applications is a challenging task. The spike neural networks inspired from physiological brain architecture, is a neuromorphic hardware implementation of network of neurons. A sample of neuromorphic architecture has two layers of neurons, input and output. The number of input neurons is fixed based on the input data patterns. While the number of outputs neurons can be different. The goal of this paper is performance evaluation of neuromorphic architecture in terms of recognition rates using different numbers of output neurons. For this purpose a simulation environment of N2S3 and MNIST handwritten digits are used. Our simulation results show the recognition rate for various number of output neurons, 20, 30, 50, 100, 200, and 300 is 70%, 74%, 79%, 85%, 89%, and 91%, respectively.