Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels

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Abstract

Fine-tuned ion transport across nanoscale pores is key to many biological processes, including neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties inaccessible at larger scales and triggering hopes of reproducing the ionic machinery of biological systems. Here we report experiments demonstrating the emergence of memory in the transport of aqueous electrolytes across (sub)nanoscale channels. We unveil two types of nanofluidic memristors depending on channel material and confinement, with memory ranging from minutes to hours. We explain how large time scales could emerge from interfacial processes such as ionic self-assembly or surface adsorption. Such behavior allowed us to implement Hebbian learning with nanofluidic systems. This result lays the foundation for biomimetic computations on aqueous electrolytic chips.

Toward fluidic neuromorphic computing

There is considerable interest in strategies that mimic the structure of human brain and could lead to the development of next-generation neuromorphic devices. Many recent studies have focused on solid-state devices, although information in biological systems is conveyed by ions solvated in water, an approach now explored in two papers in this issue (see the Perspective by Noy and Darling). Robin et al . created nanofluidic devices consisting of nanometer-thick two-dimensional slits filled with a salt solution, whereas Xiong et al . present a nanofluidic ionic memristor based on confined polyelectrolyte-ion interactions. The two studies are focused on different aspects of neuromorphic engineering, but both show precise control of ion transport in water across nanoscale channels. These studies show promising directions for creating neuromorphic functions using energy-efficient fluidic memristors that could mimic biological systems down to their fundamental principles. —YS

Abstract

Two nanofluidic devices can reproduce Hebbian learning using ions in water as charge carriers, similar to how neurons work.

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Dmitri Strukov, Gregory S. Snider, Duncan R. Stewart … (2008)

Anyone who ever took an electronics laboratory class will be familiar with the fundamental passive circuit elements: the resistor, the capacitor and the inductor. However, in 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor). Although he showed that such an element has many interesting and valuable circuit properties, until now no one has presented either a useful physical model or an example of a memristor. Here we show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current-voltage behaviour observed in many nanoscale electronic devices that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches.

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