Study of Recall Time of Associative Memory in a Memristive Hopfield Neural Network

Kong, Deyu; Hu, Shaoxiang; Wang, Junjie; Liu, Zhen; Chen, T. P.; Yu, Qin; Liu, Yang

doi:10.1109/access.2019.2915271

Cited by 15 publications

(13 citation statements)

References 34 publications

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“…The work of [ 16 ] utilized HNN for transmitting binary amplitude modulated signals based on the potential energy function yielding lower probability of error. In addition, the work of [ 17 ] emphasized HNN as one of the most studied attractor-memory models due to the feature of useful Content Addressable Memory (CAM) for an optimization model. Note that HNN can be split into continuous HNN (CHNN) and discrete HNN (DHNN).…”

Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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Section: Introductionmentioning

confidence: 99%

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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“…The review found that the use of neural networks to develop computational architectures is oriented toward the design of the networks, followed by learning algorithms to simulate different brain functions in 38.1% [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Next, the development of brain simulation software is 14.3% [ 49 , 50 , 51 ], and the development of hybrid architectures (using brain computing interfaces supported by neuromorphic processors) accounts for 14.3% [ 52 , 53 , 54 ].…”

Section: Methods and Resultsmentioning

confidence: 99%

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Hernández

Barbosa-Santillán

2022

Brain Sciences

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The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

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“…Artificial neural networks (ANNs) implemented with traditional complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs), which provide a certain level of intelligence, have been reported. [1][2][3][4] However, constructing CMOS circuits as the synapses and neurons is at the expensive costs of large chip area and power consumption. 5 A critical issue in constructing a large scale artificial neural network is to look for a suitable device structure that can be used to build neurons and synapses.…”

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confidence: 99%

Winner-takes-all mechanism realized by memristive neural network

Wang

et al. 2019

Applied Physics Letters

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Winner-takes-all (WTA), an important mechanism in neural networks of recurrently connected neurons, is a critical element of many models of cortical processing. However, few WTA neural networks have been realized physically, especially by memristor networks. In this work, we have designed and implemented a neural network with memristor-based synapses to realize the WTA in a neural system. Neuronal self-excitatory, excitatory, and inhibition by other neurons have been demonstrated. Competitions between two neurons, among three neurons, and between two groups of neurons are realized based on the memristive neural network. The winner neuron or winner group can suppress the other neuron(s) or other group(s) of neurons and dominate the neuronal firing. This work paves the way for further realization of complex models of cortical processing with memristive neural networks. Applied Physics LettersARTICLE scitation.org/journal/apl FIG. 5. Schematic illustration of competition between two groups of neurons (a) and firing characteristics for N 1 (b), N 2 (c), N 3 (d), and N 4 (e). Detailed circuits are presented in Fig. S4.

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Study of Recall Time of Associative Memory in a Memristive Hopfield Neural Network

Cited by 15 publications

References 34 publications

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Winner-takes-all mechanism realized by memristive neural network

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