A novel hardware-efficient ergodic sequential logic spiking neural network and reproductions of biologically plausible spatio-temporal phenomena towards development of neural prosthetic device

An ergodic sequential logic (SL) neuron model is designed and it is shown that the model can reproduce various nonlinear responses of neurons. Using the neuron model, a novel ergodic SL spiking neural network is presented. It is shown that the network can reproduce biologically plausible spatio-temporal phenomena, (e.g., fundamental synchronization phenomenon and complicated chimera phenomenon) which are typically observed in the brain. In addition, the network is implemented by a field programmable gate array and it is shown that the presented network is more hardware-efficient compared to a commonly used digital processor spiking neural network. Furthermore, it is discussed that the presented network will contribute to develop a small and low power brain prosthetic device.