Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits

Afifi, Ahmad; Ayatollahi, Ahmad; Raissi, Farshid

doi:10.1109/ecctd.2009.5275035

Cited by 83 publications

(47 citation statements)

References 14 publications

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“…Memristors can be used for a variety of applications such as memory [7], neuromorphic systems [8], analog circuits (e.g. see [9]), and logic design [10], [27].…”

Section: ( ) ( ) ( ) V T R W I I Tmentioning

confidence: 99%

TEAM: ThrEshold Adaptive Memristor Model

Kvatinsky

Friedman

Kolodny

et al. 2013

IEEE Trans. Circuits Syst. I

682

324

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Abstract-Memristors are novel devices, which can be used in applications such as memory, logic, and neuromorphic systems. A memristor offers several advantages to existing applications: nonvolatility, good scalability, effectively no leakage current, and compatibility with CMOS technology, both electrically and in terms of manufacturing. Several models for memristors have been developed and are discussed in this paper. Digital applications such as memory and logic require a model that is highly nonlinear, simple for calculations, and sufficiently accurate. In this paper, a new memristor model is presented -TEAM, ThrEshold Adaptive Memristor model. Previously published models are compared in this paper to the proposed TEAM model. It is shown that the proposed model is reasonably accurate and computationally efficient, and is more appropriate for circuit simulation than previously published models.

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“…Memristors can be used for a variety of applications such as memory [7], neuromorphic systems [8], analog circuits (e.g. see [9]), and logic design [10], [27].…”

Section: ( ) ( ) ( ) V T R W I I Tmentioning

confidence: 99%

TEAM: ThrEshold Adaptive Memristor Model

Kvatinsky

Friedman

Kolodny

et al. 2013

IEEE Trans. Circuits Syst. I

682

324

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“…Synapses in brains behave similarly to memristive devices, prompting the method to take advantage of memristors in neuromorphic hardware [11]. Storing and updating the connection weights of synapse based on synaptic plasticity rules is one of the most troublesome operations in biologically-inspired neural networks.…”

Section: Memristor Modelmentioning

confidence: 99%

An adaptive neural network A/D converter based on CMOS/memristor hybrid design

Wang

You

Liu

et al. 2014

IEICE Electron. Express

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A memristor is regarded as a promising device for modeling synapses in the realization of artificial neural systems for its nanoscale size, analog storage properties, low energy and non-volatility. In this letter, an adaptive T-Model neural network based on CMOS/memristor hybrid design is proposed to perform the analog-to-digital conversion without oscillations. The circuit is composed of CMOS neurons and memristor synapses. The A/D converter (ADC) is trained by the least mean square (LMS) algorithm. The conductance of the memristors can be adjusted to convert input voltages with different ranges, which makes the ADC flexible. Using memristors as synapses in neuromorphic circuits can potentially offer high density.

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“…Memristor has widely potential applications in electronic circuits, computer memory, reconfigurable computing, and so on [4][5][6][7]. Recently, implementation of memristor based chaotic circuits is an active topic of research.…”

Section: Introductionmentioning

confidence: 99%

Stabilization and Synchronization of Memristive Chaotic Circuits by Impulsive Control

et al. 2017

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The purpose of this note is to study impulsive control and synchronization of memristor based chaotic circuits shown by Muthuswamy. We first establish a less conservative sufficient condition for the stability of memristor based chaotic circuits. After that, we discuss the effect of errors on stability. Meanwhile, we also discuss impulsive synchronization of two memristor based chaotic systems. Our results are more general and more applicable than the ones shown by Yang, Li, and Huang. Finally, several numerical examples are given to show the effectiveness of our methods.

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Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits

Cited by 83 publications

References 14 publications

TEAM: ThrEshold Adaptive Memristor Model

TEAM: ThrEshold Adaptive Memristor Model

An adaptive neural network A/D converter based on CMOS/memristor hybrid design

Stabilization and Synchronization of Memristive Chaotic Circuits by Impulsive Control

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