Efficient hardware implementation of the subthalamic nucleus–external globus pallidus oscillation system and its dynamics investigation

Abstract

Modeling and implementation of the nonlinear neural system with physiologically plausible dynamic behaviors are considerably meaningful in the field of computational neuroscience. This study introduces a novel hardware platform to investigate the dynamical behaviors within the nonlinear subthalamic nucleus–external globus pallidus system. In order to reduce the implementation complexities, a hardware-oriented conductance-based subthalamic nucleus (STN) model is presented, which can reproduce accurately the dynamical characteristics of biological conductance-based STN cells. The accuracy of the presented design is ensured by the investigation of the dynamical properties including bifurcation analysis and phase portraits. Hardware implementation on a field-programmable gate array (FPGA) demonstrates that the proposed digital system can mimic the relevant biological characteristics with higher performance, which means the resource cost is cut down and the computational efficiency is improved by introducing the multiplier-less techniques including novel “shift MUL” approach and piecewise linear approximation. The central pattern generator (CPG) coupled by the presented system is also investigated, which can be applied as an embedded intelligent system in the field of neuro-robotic engineering.