A System-Level Neuromorphic Framework for Closed-Loop Brain-Computer Interfaces toward Restorative Neuroprosthetic Control

Abstract

Brain–computer interfaces (BCIs) and neuroprosthetic systems have demonstrated significant progress in decoding neural intent and enabling assistive motor and communication functions. However, achieving stable, long-term restoration of motor and autonomic function remains a central challenge in neural engineering, particularly under constraints of power efficiency, latency, and adaptability required for implantable systems. Neuromorphic computing offers a biologically inspired, event-driven computational paradigm that aligns closely with the operational requirements of closed-loop neural interfaces. This paper presents a system-level neuromorphic framework for closed-loop brain–computer interfaces aimed at restorative neuroprosthetic control. Rather than proposing a complete cognitive replacement, the framework is designed as a modular neural bypass that integrates spiking neural networks, bidirectional BCI architectures, and adaptive feedback mechanisms to support motor execution and autonomic regulation. The framework is grounded in established principles of neural engineering, neuroprosthetics, and implantable system design, with explicit consideration of biological integration, long-term stability, and ethical constraints. By articulating a coherent neuromorphic architecture tailored to closed-loop BCI operation, this work aims to provide a foundational reference for future experimental validation and translational development of neuromorphic neuroprosthetic systems within the neural engineering community.

Introduction

This paper proposes a unified neuromorphic brain–computer interface (BCI) framework aimed at restoring lost motor and autonomic functions in patients with severe neurological impairments. Unlike conventional BCIs that primarily serve as assistive tools requiring continuous user effort, the proposed system integrates neuromorphic computing as a core architectural component to enable continuous, adaptive, closed-loop neural bypass control.

The framework introduces a modular “digital brainstem” implemented through spiking neural networks (SNNs), designed to restore disrupted sensorimotor and autonomic pathways without replicating higher cognitive processes. Neuromorphic computing is emphasized for its event-driven processing, low power consumption, temporal alignment with biological neural dynamics, and support for local adaptive learning mechanisms such as spike-timing-dependent plasticity. These properties make it well suited for implantable, long-term neuroprosthetic systems.

The architecture consists of four layers: neural acquisition, neuromorphic processing, control and actuation, and sensory feedback. By incorporating real-time feedback into the spiking network, the system enables adaptive closed-loop control, improving stability, latency, and long-term functionality. A minimal computational formulation demonstrates theoretical feasibility, modeling the system as a bounded adaptive feedback network with stable closed-loop dynamics.

The paper also addresses practical constraints, including implantable hardware limitations, biological integration challenges, and material considerations. Ethical and clinical issues—such as autonomy, informed consent, and equitable access—are acknowledged. While the framework remains conceptual and not yet experimentally validated, it provides a system-level architectural foundation to guide future hardware development, simulation studies, and translational neuroengineering research.

Conclusion

This work presents an extended neuromorphic framework for brain–chip interfaces aimed at restoring motor and autonomic function through adaptive neural bypass mechanisms. By integrating spiking neural networks, closed-loop BCI architectures, and biologically informed design, the framework outlines a realistic pathway toward restorative neuroengineering. Although significant challenges remain in hardware integration, biological compatibility, and ethical governance, the convergence of neuromorphic computing and BCIs provides a strong foundation for future experimental validation. The proposed architecture is intended to serve as a foundational reference for future experimental validation, hardware–software co-design, and translational research efforts. By articulating a neuromorphic closed-loop BCI framework grounded in neural engineering principles, this work aims to support the development of clinically viable neuroprosthetic systems and to inform ongoing research within the Journal of Neural Engineering community.

References

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Copyright

Copyright © 2026 Anand Rawat, Anamika Yadav, Anand Sen, Ananya Agarwal, Anandi Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.