The spiking neural network based on fMRI for speech recognition

The structure of the human brain has evolved to achieve extraordinary computing power through continuous refinement by natural selection. At present, the topology of brain -like model lacks biological plausibility. In this paper, a new brain -like model is proposed, called fMRI-SNN, which is a spiking neural network (SNN) constrained by the topology of a functional brain network from human functional Magnetic Resonance Imaging (fMRI) data. To verify its performance, this fMRI-SNN is applied to speech recognition. Our results indicate that the recognition accuracy of fMRI-SNN is superior to that of other SNNs and reported methods, and exhibits stronger performance on more difficult speech recognition tasks. Our discussion on recognition mechanism finds the advantage of fMRI-SNN is that the differences in its neuronal firing patterns are greater than those of other SNNs, since it has better information transmission ability.