A performance evaluation of probabilistic vs. deterministic spiking neural network

This paper aims to present a comparison between probabilistic and deterministic spiking neural network for a back Propagation classification algorithm. To have a fair comparison, neuron models and structures are considered identical in both of the networks. The networks are trained and tested with the Iris database. According to the simulation results, the probabilistic network converges faster than the deterministic one, where it is also more sensitive to the input variations. The simulation results show a precision of 90% 88% for the probabilistic and the deterministic networks correspondingly, which is in consistence with the similar results for linear neural networks.