Spiking neural network research based on brain-inspired computing

Abstract

With the shortcomings of deep learning in training cost, generalization ability, interpretability, and reliability, increasing attention was concentrated on the novel neuromorphic devices. A spiking neural network can better simulate the information transmission mode of biological neurons, and has the characteristics of strong computing power and low power consumption. Therefore, this study provides an overview of the pulse neural network from the perspective of its fundamental structure and operating principle. In terms of structure optimization, there is a summary of the aspects of the encoding mode of a spiking neural network and topology structure. In addition, the deficiency and development of the spiking neural network are analyzed. Pulsed neurons work by integrating the mechanism of firing, exchanging information with the time of the signal, and transmitting signals to adjacent neurons only when the membrane potential reaches a threshold. Spiking neural network (SNN) still lags behind traditional Artificial neural network (ANN) in accuracy, has large computational capacity and is inefficiently trained due to the complexity of pulsed neuron models. Future developments lie in bionics, deeper studies of the complex dynamics of the human brain, and synaptic connections in the human brain.