Recapitulating the electrophysiological features of in vivo biological networks by using a real-time hardware Spiking Neural Network

Electroceutical methodologies utilized for treating neurological disorders, including stroke, can leverage neuromorphic engineering principles to design devices capable of seamlessly interfacing with the neural system. This paper introduces a bank of configurations for a real-time hardware Spiking Neural Network (SNN) based on the Hodgkin-Huxley formalism to mimic the electrophysiological behavior of an in vivo biological neural network (BNN). The neuronal activity in the rostral forelimb area of six anesthetized healthy rats was analyzed to extract peculiar electrophysiological features, such as the firing rate and the Inter-Spike-Interval, required for the customization of the SNN parameters. A set of different SNNs was built and comparative analyses between the electrical patterns generated by SNNs and the neural activity recorded from the BNNs were performed. The results indicate that it is possible to fine tune the SNN to achieve an electrophysiological behavior closely resembling that of a biological system.