关键词: 脉冲神经网络, 并行计算, 神经元编码, 路由表, 周期动态同步

Abstract:

Spiking neural networks are the third-generation artificial neural networks, which are more bio-interpretable neural network models. With the continuous research on spiking neural networks, not only the spatial structure of neurons is more complicated, but also the scale of neural network structure increases. In the form of serial computing, it is difficult to simulate the spiking neural network on a personal computer. To this end, a multi-core parallel spiking neural network simulator is designed to encode and map neurons. The custom routing table solves the network communication between multiple cores, and uses time-driven strategy to achieve dynamic synchronization between core and core. Parallel computing of spiking neural networks is performed on the simulator. Using Izhikevich spiking neurons as a model, experimental results in the simulation environment show that, multi-core parallel computing is about twice as efficient as traditional serial computing, provides a reference for the parallel design of similar spiking neural networks.

Key words: spiking neural network, parallel computing, neuron coding, routing table, periodic dynamic synchronization