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      Memristive Hodgkin-Huxley Spiking Neuron Model for Reproducing Neuron Behaviors

      research-article
      1 , 2 , 3 , 4 , 5 , 1 , 3 , 4 , 5 , *
      Frontiers in Neuroscience
      Frontiers Media S.A.
      HH, MHH, memristor, neuron, spiking

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          Abstract

          The Hodgkin-Huxley (HH) spiking neuron model reproduces the dynamic characteristics of the neuron by mimicking the action potential, ionic channels, and spiking behaviors. The memristor is a nonlinear device with variable resistance. In this paper, the memristor is introduced to the HH spiking model, and the memristive Hodgkin-Huxley spiking neuron model (MHH) is presented. We experimentally compare the HH spiking model and the MHH spiking model by applying different stimuli. First, the individual current pulse is injected into the HH and MHH spiking models. The comparison between action potentials, current densities, and conductances is carried out. Second, the reverse single pulse stimulus and a series of pulse stimuli are applied to the two models. The effects of current density and action time on the production of the action potential are analyzed. Finally, the sinusoidal current stimulus acts on the two models. The various spiking behaviors are realized by adjusting the frequency of the sinusoidal stimulus. We experimentally demonstrate that the MHH spiking model generates more action potential than the HH spiking model and takes a short time to change the memductance. The reverse stimulus cannot activate the action potential in both models. The MHH spiking model performs smoother waveforms and a faster speed to return to the resting potential. The larger the external stimulus, the faster action potential generated, and the more noticeable change in conductances. Meanwhile, the MHH spiking model shows the various spiking patterns of neurons.

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          Most cited references35

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          The missing memristor found.

          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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            Memristor-The missing circuit element

            L P Chua (1971)
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              Memristive devices for computing.

              Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                23 September 2021
                2021
                : 15
                : 730566
                Affiliations
                [1] 1School of Electronic and Information Engineering, Southwest University , Chongqing, China
                [2] 2College of Artificial Intelligence, Southwest University , Chongqing, China
                [3] 3Brain-Inspired Computing and Intelligent Control of Chongqing Key Lab , Chongqing, China
                [4] 4National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology , Chongqing, China
                [5] 5Chongqing Brain Science Collaborative Innovation Center , Chongqing, China
                Author notes

                Edited by: Huanglong Li, Tsinghua University, China

                Reviewed by: Jie-Ning Wu, Fudan University, China; Mauro Forti, University of Siena, Italy

                *Correspondence: Lidan Wang ldwang@ 123456swu.edu.cn

                This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2021.730566
                8496503
                34630019
                49235ac5-8d37-4493-9e85-ca867bacd20f
                Copyright © 2021 Fang, Duan and Wang.

                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) and the copyright owner(s) 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.

                History
                : 25 June 2021
                : 16 August 2021
                Page count
                Figures: 14, Tables: 0, Equations: 39, References: 35, Pages: 18, Words: 7867
                Categories
                Neuroscience
                Original Research

                Neurosciences
                hh,mhh,memristor,neuron,spiking
                Neurosciences
                hh, mhh, memristor, neuron, spiking

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