Spike sorting using non-volatile metal-oxide memristors

TL;DR

This paper presents a novel approach to spike sorting in neural data using non-volatile metal-oxide memristors, enabling ultra-compact, power-efficient analogue circuits for in-situ neural signal processing.

Contribution

It introduces the use of memristive devices for spike sorting and template matching, combining their analogue programmability with standard logic for efficient neural data analysis.

Findings

Memristors can perform spike sorting with high efficiency.

Combining memristors with logic enables compact template matching.

The approach reduces power consumption compared to digital methods.

Abstract

Electrophysiological techniques have improved substantially over the past years to the point that neuroprosthetics applications are becoming viable. This evolution has been fuelled by the advancement of implantable microelectrode technologies that have followed their own version of Moore's scaling law. Similarly to electronics, however, excessive data-rates and strained power budgets require the development of more efficient computation paradigms for handling neural data in-situ, in particular the computationally heavy task of events classification. Here, we demonstrate how the intrinsic analogue programmability of memristive devices can be exploited to perform spike-sorting. We then show how combining memristors with standard logic enables efficient in-silico template matching. Leveraging the physical properties of nanoscale memristors allows us to implement ultra-compact analogue circuits for neural signal processing at the power cost of digital.