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Published on: February 9, 2024
Learning from the brain's architecture: bioinspired strategies towards implantable neural interfaces
Nicholas J Rommelfanger1, Carl Hc Keck2, Yihang Chen2
1Department of Applied Physics, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
Abstract:
While early neural interfaces consisted of rigid, monolithic probes, recent implantable technologies include meshes, gels, and threads that imitate various properties of the neural tissue itself. Such mimicry brings new capabilities to the traditional electrophysiology toolbox, with benefits for both neuroscience studies and clinical treatments. Specifically, by matching the multi-dimensional mechanical properties of the brain, neural implants can preserve the endogenous environment while functioning over chronic timescales. Further, topological mimicry of neural structures enables seamless integration into the tissue and provides proximal access to neurons for high-quality recordings. Ultimately, we envision that neuromorphic devices incorporating functional, mechanical, and topological mimicry of the brain may facilitate stable operation of advanced brain machine interfaces with minimal disruption of the native tissue.

