Brain-inspired computing with fluidic iontronic nanochannels

TL;DR

This paper introduces fluidic nanochannels as brain-inspired memristors for neuromorphic computing, demonstrating their ability to emulate brain-like ion transport and perform time series classification.

Contribution

It presents a novel fabrication of tapered microchannels with embedded nanochannels supported by a theoretical model, enabling stable, volatile memristors for neuromorphic applications.

Findings

Devices exhibit stable volatile memristive behavior.

Memory retention time scales quadratically with channel length.

Channels successfully classify handwritten numbers in reservoir computing.

Abstract

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy to fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarisation our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarisation, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in-silico classification with a simple readout function. Our results represent a significant step towards realising the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.