Nonlinear hardware realization and fast digital approximation of the dressed neuron model for astrocyte–neuron coupling dynamics

This paper presents a high-performance, resource-efficient digital implementation of the Dressed Neuron Model (DNM), a biologically inspired system that captures bidirectional interactions between neurons and astrocytes. Unlike classical neuron models, the DNM incorporates astrocyte-mediated calcium and IP 3 signaling, forming a closed-loop feedback system capable of exhibiting spontaneous, seizure-like oscillations. To address the high computational complexity of this model on hardware, we introduce a Hybrid Model of DNM (HMoDNM), which approximates all major nonlinearities using a combination of dual-sinusoidal expressions and ROM-based lookup tables. These approximations achieve high numerical fidelity with root mean square error (RMSE) below 1 0 − 2 for most functions, while ensuring hardware efficiency. The full system is implemented on a Xilinx Zynq-7000 XC7Z010 FPGA using a shift-and-add architecture with zero DSP slice utilization. All signals are represented in a fixed-point format 〈 1 , 10 , 27 〉 , with dynamic range coverage up to 800 μ M for IP 3 and 600 μ M for calcium. The design includes pipelined neuron and astrocyte cores, clock-gated nonlinear units, and shared computation modules, achieving a maximum clock frequency of 305 MHz and throughput of 21.7 million Euler steps per second. Overall resource usage is 6690 LUTs (38.0%), 2640 FFs (7.5%), and 4 BRAMs (6.7%), with a low dynamic power consumption of 167 mW and operating temperature of 35.1 °C at room ambient. To validate the model’s functional accuracy, we compare the HMoDNM outputs against the original DNM across two dynamic regimes, achieving correlation coefficients above 94% and NRMSE values below 0.06 for membrane voltage, calcium, and IP 3 . Designed specifically for epilepsy modeling, this architecture provides a robust foundation for real-time tracking and control of astrocyte-influenced seizure dynamics. The proposed HMoDNM architecture offers a versatile foundation for hardware-based neuromorphic applications, including real-time seizure detection, closed-loop neurostimulation systems, and low-power embedded platforms for modeling neuron–glia interactions in brain-inspired computing.