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ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Neuromorphic Engineering

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1569397

This article is part of the Research TopicNovel Memristor-Based Devices and Circuits for Neuromorphic and AI Applications Volume IIView all 3 articles

Efficient Implementation of the Hodgkin-Huxley Potassium Channel via a Single Volatile Memristor

Provisionally accepted
Lennart LandsmeerLennart Landsmeer1Erbing HuaErbing Hua1Heba AbunahlaHeba Abunahla1*Ali SiddiqiAli Siddiqi2Ryoichi IshiharaRyoichi Ishihara1Chris I De ZeeuwChris I De Zeeuw3Said HamdiouiSaid Hamdioui1Christos StrydisChristos Strydis1
  • 1Delft University of Technology, Delft, Netherlands
  • 2Lahore University of Management Sciences, Lahore, Punjab, Pakistan
  • 3Erasmus Medical Center, Rotterdam, Netherlands

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In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive properties, positioning memristors as promising candidates for biologically accurate artificial neurons. Memristor-based brain simulations hold the promise of improved energy efficiency, scalability and size compared to digital simulations, potentially benefiting applications like soft robotics, embedded systems and neuroprosthetics. Previous attempts using current-controlled Mott memristors struggled to replicate Hodgkin-Huxley models, as ion channels are voltage-controlled, engaging different dynamics. Volatile, oxide-based memristors, driven by electric-field-induced, oxygen-vacancy migration, better emulate Hodgkin-Huxley voltage control. This study shows how a volatile Pt/NbOx/Ti memristor can directly implement the Hodgkin-Huxley potassium channel. Starting from theoretical memristor dynamics, we show that the device reproduces the required sigmoidal gating and voltage-dependent time constants. By fine-tuning the voltage and time scales, we faithfully replicate the potassium channel. This marks the first voltage-controlled physical memristive implementation of the Hodgkin-Huxley potassium ion channel.

Keywords: realistic brain models, memristors, Brain machine interfacing, neural networks, Simulations

Received: 31 Jan 2025; Accepted: 06 Jun 2025.

Copyright: © 2025 Landsmeer, Hua, Abunahla, Siddiqi, Ishihara, De Zeeuw, Hamdioui and Strydis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Heba Abunahla, Delft University of Technology, Delft, Netherlands

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material