Implantable FPGA-Based Neuromorphic System for Real-Time Noise Detection and Correction in Neurobionic Applications

Real-time mitigation of signal noise in neuromorphic systems is a critical requirement for developing reliable implantable bionic interfaces targeting neurological disorders. While prior hardware implementations of neuronal models on FPGA have prioritized efficiency through approximations of nonlinear dynamics, they often neglect the stochastic nature of biological noise. In this work, we present a hardware implementation capable of real-time detection and correction of transient noise events using two well-established algorithms, regardless of their timing or duration. These algorithms were validated on Hodgkin-Huxley and FitzHugh-Nagumo models and synthesized on FPGA, confirming their precision, robustness, and feasibility for real-time deployment. Beyond conventional noise suppression, the proposed system models baseline biological activity and autonomously regulates abnormal deviations, potentially reducing neural dysfunction in implanted bioelectronic devices. This approach provides a foundational step toward adaptive neurobionic systems for therapeutic applications, such as neuroprosthetics or implantable controllers for managing chronic neurological disorders.