Neural Synaptic Weighting With a Pulse-Based Memristor Circuit

Kim, Hyongsuk; Sah, Maheshwar Pd.; Yang, Changju; Roska, Tamás; Chua, Leon O.

doi:10.1109/tcsi.2011.2161360

Cited by 323 publications

(149 citation statements)

References 27 publications

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“…In 2008, researchers in Hewlett-Packard announced that a solid-state implementation of memristor has been successfully fabricated [2]. Since then, designing memory circuits has received significant attention of researchers, and many different kinds of memristor-based circuits have been designed [3][4][5]. In 2009, Ventra et al [6] reported memcapacitors and meminductors.…”

Section: Introductionmentioning

confidence: 99%

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Banerjee

2018

Complexity

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Application of conformable fractional calculus in nonlinear dynamics is a new topic, and it has received increasing interests in recent years. In this paper, numerical solution of a conformable fractional nonlinear system is obtained based on the conformable differential transform method. Dynamics of a conformable fractional memcapacitor (CFM) system is analyzed by means of bifurcation diagram and Lyapunov characteristic exponents (LCEs). Rich dynamics is found, and coexisting attractors and transient state are observed. Symbol complexity of the CFM system is estimated by employing the symbolic entropy (SybEn) algorithm, symbolic spectral entropy (SybSEn) algorithm, and symbolic C 0 (SybC 0 ) algorithm. It shows that pseudorandom sequences generated by the system have high complexity and pass the rigorous NIST test. Results demonstrate that the conformable memcapacitor nonlinear system can also be a good model for real applications.

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Section: Introductionmentioning

confidence: 99%

Chaos and Symbol Complexity in a Conformable Fractional‐Order Memcapacitor System

Banerjee

2018

Complexity

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“…3 Hence, the conductance can be precisely controlled by voltage pulses with different widths, amplitudes and shapes, [4][5][6] which allows artificially mimicking synaptic functionalities. [7][8][9][10] Synapses and the modification of their strength are crucial for learning and memory in neural networks. 11,12 A model called spike-timingdependent plasticity (STDP) relates this modification to the time difference between incoming pre-and postsynaptic action potentials, [13][14][15][16] which allows to detect the coincidence of two or more input signals.…”

Section: Introductionmentioning

confidence: 99%

Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

Maier

Hartmann

Dias

et al. 2016

Journal of Applied Physics

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We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.

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“…1(a), the memristor possesses significant potential for such next-generation nonvolatile memories [3]. Over the past several years, memristors have been applied in many fields, such as analog circuits [4], digital information progressing [5], neuromorphic [6], resistive random access memory (RRAM) [7], and microwave devices [8]. For all applications, a simple and straightforward theoretical model of the memristor is needed before physical realization.…”

Section: Introductionmentioning

confidence: 99%