ArticleJournal of the American Chemical Society2025
Relaxation Time of Multipore Nanofluidic Memristors for Neuromorphic Applications.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spiking without Resets: Continuous Integrate-and-Fire Dynamics in Neuronal Circuits.The journal of physical chemistry letters · 2026Article
- Bioinspired Nanofluidic Memristors Based on Polyelectrolyte Conformation for Synaptic Learning and in-Memory Logic Computing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Observation of the transition from capacitive to inductive synaptic behavior.National science review · 2026Article
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Ionic species programmable synaptic plasticity in multimodal nanofluidic devices.National science review · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Memristors have been positioned at the forefront of the purposes for carrying out neuromorphic computation. Their tunable conductance properties enable the imitation of synaptic behavior. Nanofluidic memristors made of multipore membranes have shown their memristic properties and are candidate devices for liquid neuromorphic systems. Such properties are visible through an inductive hysteresis in the current-voltage sweeps, which is then confirmed by the inductive characteristics in impedance spectroscopy measurements. The dynamic behavior of memristors is largely determined by a voltage-dependent relaxation time. Here, we obtain the kinetic relaxation time of a multipore nanofluidic memristor via its impedance spectra, modeling it and deriving a general equation for this time as a function of the applied voltage, fully correlated with the system's internal parameters. We show that the behavior of this characteristic of memristors is comparable to that of natural neural systems. Hence, we open a way to study the mimic of neuron characteristics by searching for memristors with the same kinetic times.
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Registered trials
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