Cybertissue Neuroprosthetics: a new implantable neuroprosthetics paradigm to achieve seamless lifetime integration

Abstract Despite decades of advances in neural interface technology, the long-term instability of implantable neuroprostheses remains a major barrier to achieving reliable lifetime performance. Chronic foreign-body reactions and fibrotic encapsulation progressively degrade the electrode-tissue interface, leading to signal deterioration and eventual device failure. Existing strategies that focus on improving material biocompatibility, reducing mechanical mismatch, or modifying implant surfaces have produced important but largely incremental improvements, while leaving the fundamental challenge unresolved. This perspective paper introduces a fundamentally different paradigm: Cybertissue Neuroprosthetics (CN). CN moves beyond the traditional electrode‑based neural interface by treating the implant itself as a functional tissue device—composed of living cells and optionally augmented with minimal nanoelectronics, nanomaterials, or genetic modifications—designed to integrate structurally and functionally with the host nervous system to achieve long‑term neuromodulation and information exchange. By minimizing or eliminating foreign materials and leveraging the brain’s own cellular signaling mechanisms, CN promises to overcome the inherent limitations of current inorganic neuroprosthetic devices and seeks to achieve seamless lifetime integration between neuroprosthetic devices and the nervous system. This paper introduces the CN concept, unifies a range of emerging research directions under this umbrella, and articulates the core opportunities—from engineered cybercells to transplantable living devices—that collectively define a new interdisciplinary frontier. We argue that CN represents a foundational shift in neuroprosthetics, with the potential to deliver truly lifetime neural implants that are biologically integrated, self‑sustaining, and clinically transformative.

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Authors

Liang Guo

Published

Oct 14, 2026

Journal or venue

Journal of Neural Engineering

DOI

10.1088/1741-2552/aeb442

Paper type

journal article

Source

Crossref