• Theory, Research and Development •

### SNN Training Algorithm Based on Relationship Between Pulse Frequency and Input Current

LAN Haoxin, CHEN Yunhua

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
• Online:2022-05-15 Published:2022-05-15

### 基于脉冲频率与输入电流关系的SNN训练算法

1. 广东工业大学 计算机学院，广州 510006

Abstract: Spiking neural network（SNN） is driven by asynchronous events and supports massively parallel computing. It has great potential in improving the computational efficiency of synchronous analog neural networks. However, SNN is still facing the problem that it cannot be directly trained. For this reason, inspired by the research on the response mechanism of leaky integrate-and-fire（LIF） model, this paper proposes a new SNN training algorithm based on rate coding. Firstly, this paper models the spike rate of the LIF neuron through the simulation experiment, obtains the expression relationship between the spike rate and the input current, and takes the derivative as the gradient, which solves the non-differentiability of the discrete spike event during the SNN training process. Secondly, this paper uses the LIF neuron response mechanism to update the network parameters, which improves the learning efficiency. The experimental results on the MNIST and CIFAR10 datasets verify the effectiveness of this method. The classification accuracy reaches 99.53% and 89.46%. The recognition accuracy on CIFAR10 data is 4.22 percentage points higher than that of previous researchers and the learning efficiency has been doubled.