Optimized Spiking Neural Network for Chaotic System Prediction in FPGA

The Spike Neural Network (SNN) is a neural network that simulates the transmission of signals in brain cells. It utilizes the low-energy characteristics of neurons transmitting discrete signals, which have attracted widespread attention. This paper proposes an SNN with the threshold voltage drop to optimize the SNN. The model focuses on controlling the threshold voltage to reduce the effect of residual membrane voltage in the Mean Square Error (MSE). Reduced threshold voltage stimulates neurons to fire more spikes. The initialized membrane voltage SNN (I-SNN) is an optimized method that adding an extra membrane voltage in neurons. This paper optimizes the I-SNN to process negative data, introducing a variant called the signed initialized membrane voltage SNN (SI-SNN). The threshold voltage drop SNN and the SI-SNN in different numbers of neurons are compared for MSE in predicting the Lorenz System. The results indicate that the SNN with threshold voltage drop effectively reduces MSE, especially in the model with fewer neurons. After confirming the feasibility of this algorithm, this paper implements the threshold voltage drop method in FPGA. The MSEs of no optimized SNN and threshold voltage drop in FPGA are 0.0015 and 0.0013, respectively. The threshold voltage drop SNN demonstrates satisfactory performance.