Novel designs of spiking neuron circuit and STDP learning circuit based on memristor

Zhao, Lina; Hong, Qinghui; Wang, Xiaoping

doi:10.1016/j.neucom.2018.06.062

Cited by 39 publications

(18 citation statements)

References 20 publications

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“…Generally, to implement STDP with memristors an approach where pre- and post-spikes are superposed to induce synaptic weight change in memristors is adopted. In such approaches input voltage signals (spikes) are directly applied to the devices which can expose them to CMOS circuit non-idealities 35,66 . Since the memristive devices response to shape and frequency of an incoming spikes, time-modulated amplitude of superposed spikes or similar techniques are also adopted 66,67 .…”

Section: Resultsmentioning

confidence: 99%

“…In such approaches input voltage signals (spikes) are directly applied to the devices which can expose them to CMOS circuit non-idealities 35,66 . Since the memristive devices response to shape and frequency of an incoming spikes, time-modulated amplitude of superposed spikes or similar techniques are also adopted 66,67 . However, in this work the relative weight change is achieved by a fully digital spike processing unit (time-to-digital-to-voltage circuit, discussed below) which offers a higher level of multiplexing and lower complexity of the overall drive circuitry.…”

Section: Resultsmentioning

confidence: 99%

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Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Ahmed

Walia

Mayes

et al. 2019

Sci Rep

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Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO3-x (STOx). The potential of such hybrid CMOS-memristor approach is demonstrated by successfully imitating time-dependent (pair and triplet spike-time-dependent-plasticity) and rate-dependent (Bienenstosk-Cooper-Munro) synaptic learning rules. Experimental results are benchmarked against in-vitro measurements from hippocampal and visual cortices with good agreement. The scalability of synaptic devices and their programming through a CMOS drive circuitry elaborates the potential of such an approach in realizing adaptive neuromorphic computation and networks.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Ahmed

Walia

Mayes

et al. 2019

Sci Rep

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“…Chaos is a typical nonlinear system that is generated by deterministic systems and is sensitive to initial conditions. Designing a novel chaotic system and studying its dynamic properties can be helpful to expand the scope of chaos research and increase the applicability of chaotic systems to different fields 4‐8 . Compared with typical chaotic system, the chaotic signal generated by a memristor chaotic system has stronger pseudo‐randomness and thus can be more useful in various scientific and engineering applications.…”

Section: Introductionmentioning

confidence: 99%

“…Designing a novel chaotic system and studying its dynamic properties can be helpful to expand the scope of chaos research and increase the applicability of chaotic systems to different fields. [4][5][6][7][8] Compared with typical chaotic system, the chaotic signal generated by a memristor chaotic system has stronger pseudorandomness and thus can be more useful in various scientific and engineering applications. Therefore, it is of great practical significance to study memristor chaotic systems with excellent performance.…”

Section: Introductionmentioning

confidence: 99%

A novel dual memristor hyperchaotic system and its application for secure communication based on three‐fold function projection synchronization

Jiang

Deng

et al. 2020

Int J Numerical Modelling

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A novel simplest four‐dimensional hyperchaotic system with dual memristors is designed by using a charge‐controlled memristor and a generalized voltage‐controlled memristor. Based on the conventional dynamic analysis method and MATLAB simulation software, the dynamic characteristics of the dual memristor hyperchaotic system such as Lyapunov exponent spectrum, phase diagram, and bifurcation diagram are studied. The novel system is found to exhibit singular nonlinear phenomena, such as limit cycle, state transition and multiple scroll attractor with certain parameter values. Being different from most known chaotic systems, which contain only two isolated unstable equilibrium points, the novel system has an infinite number of unstable equilibriums points and a larger Lyapunov exponent. What is more, the three‐fold function projection synchronization of the dual memristor hyperchaotic system and its application for secure communication is studied. Based on Lyapunov stabilization theory and adaptive sliding mode control method, three driving systems and three response systems are synchronized according to the corresponding scaling function matrix, respectively. In the secure communication system, the message signal is separated into three parts loaded in the different drive systems with different initial conditions to increase the reliability. The corresponding numerical simulations demonstrate the effectiveness of the results obtained.

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“…[1][2][3] Furthermore, their analogous nature (the ability to program any value of resistance from a range) draws attention to them as a possible basis for nonconventional computing, such as physical realizations of artificial neural networks. [4][5][6][7][8][9][10] One of the widespread types of memristive devices is redoxswitching polymer elements, whose resistance is gradually controlled by electrochemical reactions. [11][12][13][14] The use of polymer spectroscopy, and spectrophotometry.…”

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confidence: 99%

Optical Monitoring of the Resistive States of a Polyaniline‐Based Memristive Device

Lapkin

Коровин

Малахов

et al. 2020

Adv Elect Materials

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materials makes the elements cheap and easy to manufacture. Moreover, organic materials allow simple chemical modification of the electrophysical properties and fabrication of flexible and stochastic 3D elements. [15-18] One of the first and most studied types of the organic memristive devices are polyaniline (PANI) based devices. [19-26] The structure and electrophysical properties of the PANI-based memristive devices are described elsewhere. [20] The main active component of the device is a thin PANI film connecting two metal electrodes. The third electrode is a silver wire separated from the film by a layer of solid electrolyte. The silver wire ("gate") is always connected to one of the substrate electrodes ("drain"), while the voltage is applied to the second one ("source"), as shown in Figure 1a. The RS is driven by a voltage-controlled electrochemical reaction at the PANI-electrolyte interface. The device switches to the low resistive state (LRS) under applied voltage exceeding the value of +0.4 V and to the high resistive state (HRS) under applied voltage less than +0.1 V. Its conductivity remains quite stable in the window between these values. Nonzero current at zero bias voltage is an essential and inevitable feature of all redox-based memristive devices. [27] The devices were shown to be stable for at least 10 4 switching cycles and able to hold each programmed resistive state for at least 10 3 s. [28,29] A detailed description of the switching mechanism is previously published. [21,22] During the RS, the insulating leucoemeraldine form of PANI transforms into the conductive emeraldine salt form and vice versa according to the following equation

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Novel designs of spiking neuron circuit and STDP learning circuit based on memristor

Cited by 39 publications

References 20 publications

Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Time and rate dependent synaptic learning in neuro-mimicking resistive memories

A novel dual memristor hyperchaotic system and its application for secure communication based on three‐fold function projection synchronization

Optical Monitoring of the Resistive States of a Polyaniline‐Based Memristive Device

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