Sub 100nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes

TL;DR

This paper introduces a nano-scale volatile memristor that efficiently detects, encodes, and compresses neural spike data with ultra-low power consumption, mimicking synaptic functions for advanced neural interfaces.

Contribution

It presents a novel volatile memristor device capable of real-time neural spike detection and encoding with sub-100 nanowatt power, enabling scalable, energy-efficient neural data processing.

Findings

Power dissipation less than 100 nW during operation

Effective encoding of neural spike amplitude and frequency

Potential for scalable, on-chip neural data processing

Abstract

Advanced neural interfaces mediate a bio-electronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require on-line processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multi-electrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.