Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.

Authors

Fan Mo, Xiaoyu Zhao, Yuanhong Xu, Chengxuan Tang, Dalin Zhang, Sai Li, Dingyuan Chen, Wenzhi Li, Zhaohui Song, Shaoqi He

Published in

iScience. Volume 29. Issue 9. Pages 117268. Sep 18, 2026. Epub Aug 22, 2026.

Abstract

Memory prosthetics, closed-loop brain-computer interfaces that decode hippocampal activity and deliver adaptive stimulation, are transitioning from animal proof-of-concept to first-in-human trials. Realizing chronically implantable systems requires co-design of three materials-mediated subsystems whose structure-property-processing (SPP) relationships have been treated in isolation: biocompatible electrode interfaces, on-chip neuromorphic computation, and closed-loop control hardware. This review presents an integrated framework. We map neuroscientific findings (theta-phase tracking, theta-gamma coupling, sharp-wave ripple detection) onto engineering specifications for latency, sampling, and charge injection, and onto materials requirements for impedance, switching endurance, and chronic stability. We develop an SPP taxonomy of two dominant materials families: chronic electrode coatings (Pt-Ir, IrOx, PEDOT:PSS, carbon-based, MXene) and oxide memristive synapses (Al2O3/TiO2-x, SrTiO3, HfO2). We further distinguish established findings from emerging directions and flag where small-cohort clinical results have been over-generalized. This synthesis provides materials-design targets for next-generation memory-prosthetic hardware.

PMID:
42668603
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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