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