Cost-efficient FPGA implementation of a biologically plausible dopamine neural network and its application

Dopamine neurons play an essential role in terms of cognitive coordination and executive functions, which has been investigated in the therapy of multiple psychiatric and neurodegenerative disorders, such as schizophrenia and Parkinson's disease (PD). This paper first explores a series of efficient methods for the hardware implementation of dopamine neuron model aiming to reproduce relevant biological behaviours. In addition, a modified dopamine neuron model based on piecewise linearisation is presented for efficient realisation to reduce the hardware overhead of the original dopamine model and improve the feasibility of the digital design, which is significant for the large-scale network emulation of dopamine system. The accuracy of hardware implementation is validated in terms of dynamical behaviours and bifurcation analyses, and the simulation results including ion channel properties and compensation effect of N-methyl-D-aspartate (NMDA) and γ-Aminobutyric acid (GABA) activation, coincide with the biological dopamine neuron model with a high accuracy. Hardware synthesis and physical implementation on Field Programmable Gate Array (FPGA) illustrate that the proposed model has reliable performance and lower hardware costs compared to original model. These investigations are conducive to construct large FPGA-based network to explore the neurophysiological mechanisms of dopamine system.