FPGA implementation of hippocampal spiking network and its real-time simulation on dynamical neuromodulation of oscillations

Neural information is represented and transmitted among single neurons by a series of all-or-none neural codes with certain oscillation dynamics. Real-time implementation of the hippocampal spiking network is a promising avenue to investigate the complexity underlying spatiotemporal information encoding and the emergent coherence that arises with the properly coupling of large number of neurons. This paper presents a real-time scalable hardware platform for implementing hippocampal spiking neural network (HSNN) with 10 K neurons, which introduces a novel network-on-chip architecture for the randomly connected spiking neural networks (SNNs). The effects of endogenous surroundings and neural heterogeneity are taken into consideration in the hardware design, which replicates more relevant biological dynamics in comparison with the state-of-the-art studies. Based on the hardware synthesis and theoretical analysis, it is demonstrated that the proposed implementation is able to mimic hippocampal oscillation modulation dynamics under external stimuli, which is vital for the reasonable design of noninvasive electrotherapeutic strategies. The proposed implementation is meaningful for both the efficient hardware implementation of the randomly connected SNNs and the dynamic investigation of the HSNNs.