A comparison for probabilistic spiking neuron model and spiking integrated and fired neuron model

As the third generation of artificial neural networks (ANNs), spiking neural networks (SNNs) have many advantages over the traditional ones. Selecting proper spiking neuron models for the design of SNNs is important. In this paper, schematic and training algorithm of spiking integrated and fired (IAF) neuron model and probability spiking neuron model (pSNM) are introduced. By comparing the classification results for mobile robots´ corridor-scene-classifier based on IAF model and pSNM, and the control results of mobile robots´ wall-following controller based on spiking IAF model and pSNM, the similarities and differences between the two models are discussed. The similar and different features of the two spiking neuron models are obtained. IAF model is more suitable for the design of mobile robots controller than that of pSNM. While pSNM has better noise robust than IAF model. Spiking IAF model and pSNM are suitable for different situations.