Morris-Lecar model of third-order barnacle muscle fiber is made of volatile memristors

Rajamani, V. S.; Kim, Hyongsuk; Chua, Leon O.

doi:10.1007/s11432-017-9374-6

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…According to the United Nations Office on Drugs and Crime (UNODC), just 0.2% of the activities can be detected [2]. Despite advances in computation [3][4][5][6][7][8][9][10], ML detection remains challenging as a complex behavioural, computational, socioeconomic, and managerial problem. In one of the first studies combining domain knowledge in anti-money laundering and data mining, Zdanowicz [9] proposed an approach for outlier de-tection in under-and over-invoicing.…”

Section: Introduction and Methodsmentioning

confidence: 99%

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Preventing Money Laundering using AI Agents

Singh¹

2022

Preprint

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Although some predefined heuristics are used they aren’t restrictive enough, still leaving to much work for human analyzers. This motivates the need for intelligent systems that can help financial institutions fight money laundering in a diversity of ways, such as: intelligent filtering of bank operations, intelligent analysis of suspicious operations, learning of new detection and analysis rules. In this paper, we present a multiagent based approach to deal with the problem of money laundering by defining a multiagent system designed to help financial institutions in this task, helping them to deal with two main problems: volume and rule improvement.

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Section: Introduction and Methodsmentioning

confidence: 99%

“…Therefore, more specific meth-ods have been presented in order to detect local outliers. In [10], outliers have been detected with peer group analysis techniques. Some papers applied a cluster-based approach consisting of unsupervised learning techniques such as k-means.…”

Section: Introduction and Methodsmentioning

confidence: 99%

Preventing Money Laundering using AI Agents

Singh¹

2022

Preprint

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“…In general, continuous system-based neuron models with more complex descriptive equations were based on biophysical assumptions [20][21][22][23], whereas discrete map-based neuron models with simpler descriptive equations were based on dynamical assumptions [24][25][26][27]. Besides, the discrete map-based models are more suitable for digital circuit implementation than the continuous system-based models [28,29].…”

Section: Introductionmentioning

confidence: 99%

Memristor-induced mode transitions and extreme multistability in a map-based neuron model

Bao

Cai

et al. 2022

Nonlinear Dyn

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Because of the advent of discrete memristor, memristor effect in discrete map has become the important subject deserving discussion. To this end, this paper constructs a memristor-based neuron model considering magnetic induction by combining an existing map-based neuron model and a discrete memristor with absolute value nonlinear memductance. Taking the coupling strength and initial state of the memristor as variables, complex mode transition behaviors induced by the introduced memristor are disclosed using numerical methods, including spiking-bursting behaviors, mode transition behaviors, and hyperchaotic spiking behaviors. In particular, all of these behaviors are greatly dependent on the memristor initial state, resulting in the existence of extreme multistability in the memristive neuron model.Therefore, this memristive neuron model can be used to effectively imitate the magnetic induction effects when complex mode transition behaviors appear in the neuronal action potential. Besides, an FPGAbased hardware platform is developed for implementing the memristive neuron model and various spiking-bursting sequences are experimentally captured therein. The results show that when biophysical memory effect is present, the memristive neuron model can better represent the ring activities of biological neurons than the original map-based neuron model.

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“…1 shows the resistive memory model for a memristor, in which the resistance of the model is coupled with the voltage across it. Interestingly, there have been previous papers which have supported the positive impact of advancement in resistive memory on deep learning, particularly computer vision further proposing the impact in medical and sport sciences as well [5][6][7][8][9][10][11][12][13][14][15]. In this work, we perform dual ion beam sputtering and also fabricate a metal oxide memristor consisting of the format: Al/ZnO/Al.…”

Section: Introductionmentioning

confidence: 99%

Modelling Simulation of Resistive RAM devices for Analog and Digital Paradigms: An Experimental Review

Jianyi¹

2022

Preprint

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Over the years, with the introduction of non-volatile memory devices such as memristors and other CMOS relevant devices, the ability to manufacture hardware which can emulate the properties of a biological synapse has become a realistic possibility. As a result, this has lead to an improvement in the corresponding computer vision and the machine learning applications from a software perspective which can support the advancement in resistive memory technology such as using memristors for neuromorphic applications. However, the increasing complexity of deep learning models has increased the demand for more sophisticated hardware architecture which enables the full potential of such complex networks on devices with resource constraints. In this work, we perform dual ion beam sputtering and also fabricate a metal oxide memristor consisting of the format: Al/ZnO/Al. We found that our memristor displayed characteristics such as bipolar resistive switching characteristics. Moreover, upon investigation, we observed that a partially amorphous thin film of ZnO and the suitable number if vacancies of oxygen help greatly in inducing the memristor to have reliable and stable behaviour in the memory cell regions. Furthermore, upon further investigation, we found that the oxide ions in the lattice can play a significant role in changing in resistance of the memristor. The endurance of the memristor had good retention and reliable voltage difference for over 300 cycles.

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Morris-Lecar model of third-order barnacle muscle fiber is made of volatile memristors

Cited by 21 publications

References 4 publications

Preventing Money Laundering using AI Agents

Preventing Money Laundering using AI Agents

Memristor-induced mode transitions and extreme multistability in a map-based neuron model

Modelling Simulation of Resistive RAM devices for Analog and Digital Paradigms: An Experimental Review

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