Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices

Abstract

Intra-cortical brain-machine interfaces (BMIs), able to decode neural activity in real-time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single-neuron spike detection require high processing rates and power, hindering the up-scaling for neurons-population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold-based neural activity detection. MIS leverages analogue multi-state switching properties of metal-oxide RRAM to compress neural inputs by encoding above-threshold events in resistance displacement, facilitating efficient data down-sampling in the post-processing, enabling low-power, high-channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi-unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real-time neuro-prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal-oxide devices: Pt/TiO_x/Pt and TiN/HfO_x/TiN, proving its platform-agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal-oxide-semiconductor (CMOS) front-ends. This eMUA-MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.