Digital neuromorphic implementation of the biologically inspired pallidal oscillator

This paper proposes a modified biologically conductance-based pallidal oscillator model, targeting low-cost and multiplierless implementation with relevant and reliable dynamical characteristics on digital neuromorphic platform. High-Accuracy neural computation is limited in scale and efficiency by available hardware resources, so there are significant demands for costefficient hardware circuits in the large-scale simulations of neuromorphic field. Thus, the feasibility of a digital implementation with lower hardware overhead cost is investigated in this paper. Implementation results on a field-programmable gate array device demonstrate that the presented model can reduce the hardware resource cost significantly compared to the conventional look-up-Table-based design. The proposed methology is an essential step towards the real-Time implementation of large-scale spiking neural network, and is meaningful for the investigation on the neurodegenerative diseases and its model-based closed-loop control. It can also be applied in the real-Time control of the bio-inspired neurorobotics.