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Condensed Matter > Soft Condensed Matter

Title: Brain-inspired computing with fluidic iontronic nanochannels

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.
Subjects: Soft Condensed Matter (cond-mat.soft); Neurons and Cognition (q-bio.NC)
Journal reference: Proceedings of the National Academy of Sciences (2024), Vol 121, Issue 18
DOI: 10.1073/pnas.2320242121
Cite as: arXiv:2309.11438 [cond-mat.soft]
  (or arXiv:2309.11438v2 [cond-mat.soft] for this version)

Submission history

From: Tim Kamsma [view email]
[v1] Wed, 20 Sep 2023 16:13:18 GMT (5248kb,D)
[v2] Thu, 25 Apr 2024 10:54:54 GMT (5606kb,D)

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