Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis

The basal ganglia (BG) comprise multiple subcortical nuclei, which are responsible for cognition and other functions. Developing a brain-machine interface (BMI) demands a suitable solution for the real-time implementation of a portable BG. In this study, we used a digital hardware implementation of a BG network containing 256 modified Izhikevich neurons and 2048 synapses to reliably reproduce the biological characteristics of BG on a single field programmable gate array (FPGA) core. We also highlighted the role of Parkinsonian analysis by considering neural dynamics in the design of the hardware-based architecture. Thus, we developed a multi-precision architecture based on a precise analysis using the FPGA-based platform with fixed-point arithmetic. The proposed embedding BG network can be applied to intelligent agents and neurorobotics, as well as in BMI projects with clinical applications. Although we only characterized the BG network with Izhikevich models, the proposed approach can also be extended to more complex neuron models and other types of functional networks. We engineer digital basal ganglia with a hybrid nucleus.We propose a cost-efficient method with real-time computational speed.The network dynamics are highlighted in the design in a biorealistic manner.Parkinsonian and resource-cost criteria are introduced for hardware evaluation.A novel multi-clock domain approach is used in the multi-module architecture.