Chemical and electrical synapse-modulated dynamical properties of coupled neurons under magnetic flow

Mostaghimi, Soudeh; Nazarimehr, Fahimeh; Jafari, Sajad; Ma, Jun

doi:10.1016/j.amc.2018.11.030

“…It is all known that abundant collective behaviors appear in the actual neural system due to the interactions in neurons [153,154]; among them, synchronization is the outstanding collective features in neuroscience [155][156][157], which is regarded as one of the mechanisms to propagate and to code information in brain [158,159]. However, there are different kinds of brain disorder diseases, such as Alzheimer's, epilepsy, Parkinson's, and schizophrenia, which are involved with the abnormal activities of synchronization [160].…”

Section: Synchronization Application Of Memristor-coupled Systemmentioning

confidence: 99%

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

Zhang

¹

,

Ping

²

,

Zhao

³

2021

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Due to the introduction of memristors, the memristor-based nonlinear oscillator circuits readily present the state initial-dependent multistability (or extreme multistability), i.e., coexisting multiple attractors (or coexisting infinitely many attractors). The dimensionality reduction modeling for a memristive circuit is carried out to realize accurate prediction, quantitative analysis, and physical control of its multistability, which has become one of the hottest research topics in the field of information science. Based on these considerations, this paper briefly reviews the specific multistability phenomenon generating from the memristive circuit in the voltage-current domain and expounds the multistability control strategy. Then, this paper introduces the accurate flux-charge constitutive relation of memristors. Afterwards, the dimensionality reduction modeling method of the memristive circuits, i.e., the incremental flux-charge analysis method, is emphatically introduced, whose core idea is to implement the explicit expressions of the initial conditions in the flux-charge model and to discuss the feasibility and effectiveness of the multistability reconstitution of the memristive circuits using their flux-charge models. Furthermore, the incremental integral transformation method for modeling of the memristive system is reviewed by following the idea of the incremental flux-charge analysis method. The theory and application promotion of the dimensionality reduction modeling and multistability reconstitution are proceeded, and the application prospect is prospected by taking the synchronization application of the memristor-coupled system as an example.

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“…It is often organically combined with other circuit elements to construct application circuits, such as memristive chaotic circuits [5][6][7], memristive neuromorphic circuits [8][9][10] and so on [11]. In particular, memristive neuromorphic circuits can mimic the functions of biological neurons and neural networks, which can exhibit rich and complex firing mechanisms, like spike [12], burst [13][14][15], cycle [16] and chaos [17][18][19][20]. These studies on firing mechanism provide theoretical basis and guidance for practical physiological experiments, which is helpful to promote the development of neuromedicine and neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Firing mechanism based on single memristive neuron and double memristive coupled neurons

Shen

¹

,

Yu

²

,

Wang

³

et al. 2022

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Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh-Nagumo neuron and Hindmarsh-Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects.

show abstract

“…It is often organically combined with other circuit elements to construct application circuits, such as memristive chaotic circuits [5][6][7], memristive neuromorphic circuits [8][9][10] and so on [11]. In particular, memristive neuromorphic circuits can mimic the functions of biological neurons and neural networks, which can exhibit rich and complex firing mechanisms, like spike [12], burst [13][14][15], cycle [16] and chaos [17][18][19][20]. These studies on firing mechanism provide theoretical basis and guidance for practical physiological experiments, which is helpful to promote the development of neuromedicine and neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Firing Mechanism Based on Single Memristive Neuron and Double Memristive Coupled Neurons

Yu

¹

,

Shen

²

,

Wang

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh-Nagumo neuron and Hindmarsh-Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects.

show abstract

Chemical and electrical synapse-modulated dynamical properties of coupled neurons under magnetic flow

Cited by 37 publications

References 29 publications

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

Firing mechanism based on single memristive neuron and double memristive coupled neurons

Firing Mechanism Based on Single Memristive Neuron and Double Memristive Coupled Neurons

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