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Xu Q, Wang K, Shan Y, Wu H, Chen M, Wang N. Dynamical effects of memristive electromagnetic induction on a 2D Wilson neuron model. Cogn Neurodyn 2024; 18:645-657. [PMID: 38699611 PMCID: PMC11061083 DOI: 10.1007/s11571-023-10014-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/04/2023] [Accepted: 09/16/2023] [Indexed: 05/05/2024] Open
Abstract
Electromagnetic induction plays a crucial impact on the firing activity of biological neurons, since it exists along with the mutual effect between membrane potential and ions transport. Flux-controlled memristor is an available candidate in characterizing the electromagnetic induction effect. Different from the previously reported literature, a non-ideal flux-controlled memristor with cosine mem-conductance function is employed to determine the periodic magnetization and leakage flux processes in neurons. Thereafter, a three-dimensional (3D) memristive Wilson (m-Wilson) neuron model is constructed under the consideration of this kind of electromagnetic induction. Numerical simulations are performed by multiple numerical tools, which demonstrate that the 3D m-Wilson neuron model can generate abundant firing activities. Interestingly, coexisting firing activities, antimonotonicity, and firing frequency regulation are discovered under special parameter settings. Furthermore, a PCB-based analog circuit is designed and hardware measurements are executed to verify the numerical simulations. These explorations in numerical and hardware surveys might provide insights to regulate the firing activities by appropriate electromagnetic induction.
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Affiliation(s)
- Quan Xu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
| | - Kai Wang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
| | - Yufan Shan
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
| | - Huagan Wu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
| | - Mo Chen
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
| | - Ning Wang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159 People’s Republic of China
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Ma M, Lu Y. Synchronization in scale-free neural networks under electromagnetic radiation. CHAOS (WOODBURY, N.Y.) 2024; 34:033116. [PMID: 38457847 DOI: 10.1063/5.0183487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/16/2024] [Indexed: 03/10/2024]
Abstract
The functional networks of the human brain exhibit the structural characteristics of a scale-free topology, and these neural networks are exposed to the electromagnetic environment. In this paper, we consider the effects of magnetic induction on synchronous activity in biological neural networks, and the magnetic effect is evaluated by the four-stable discrete memristor. Based on Rulkov neurons, a scale-free neural network model is established. Using the initial value and the strength of magnetic induction as control variables, numerical simulations are carried out. The research reveals that the scale-free neural network exhibits multiple coexisting behaviors, including resting state, period-1 bursting synchronization, asynchrony, and chimera states, which are dependent on the different initial values of the multi-stable discrete memristor. In addition, we observe that the strength of magnetic induction can either enhance or weaken the synchronization in the scale-free neural network when the parameters of Rulkov neurons in the network vary. This investigation is of significant importance in understanding the adaptability of organisms to their environment.
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Affiliation(s)
- Minglin Ma
- School of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan 411105, China
| | - Yaping Lu
- School of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan 411105, China
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Lai Q, Yang L. Hyperchaos of neuron under local active discrete memristor simulating electromagnetic radiation. CHAOS (WOODBURY, N.Y.) 2024; 34:013145. [PMID: 38285719 DOI: 10.1063/5.0182723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/02/2024] [Indexed: 01/31/2024]
Abstract
Memristor enables the coupling of magnetic flux to membrane voltage and is widely used to investigate the response characteristics of neurons to electromagnetic radiation. In this paper, a local active discrete memristor is constructed and used to study the effect of electromagnetic radiation on the dynamics of neurons. The research results demonstrate that increasing electromagnetic radiation intensity could induce hyperchaotic attractors. Furthermore, this neuron model generates hyperchaotic and three points coexistence attractors with the introduction of the memristor. A digital circuit is designed to implement the model and evaluate the randomness of its output sequence. Neuronal models exhibit a rich dynamic behavior with electrical radiation stimulation, which can provide new directions for exploring the production mechanisms of certain neurological diseases.
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Affiliation(s)
- Qiang Lai
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 3300113, People's Republic of China
| | - Liang Yang
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 3300113, People's Republic of China
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Yu F, Lin Y, Xu S, Yao W, Gracia YM, Cai S. Dynamic Analysis and FPGA Implementation of a New Fractional-Order Hopfield Neural Network System under Electromagnetic Radiation. Biomimetics (Basel) 2023; 8:559. [PMID: 38132498 PMCID: PMC10741897 DOI: 10.3390/biomimetics8080559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/04/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Fractional calculus research indicates that, within the field of neural networks, fractional-order systems more accurately simulate the temporal memory effects present in the human brain. Therefore, it is worthwhile to conduct an in-depth investigation into the complex dynamics of fractional-order neural networks compared to integer-order models. In this paper, we propose a magnetically controlled, memristor-based, fractional-order chaotic system under electromagnetic radiation, utilizing the Hopfield neural network (HNN) model with four neurons as the foundation. The proposed system is solved by using the Adomain decomposition method (ADM). Then, through dynamic simulations of the internal parameters of the system, rich dynamic behaviors are found, such as chaos, quasiperiodicity, direction-controllable multi-scroll, and the emergence of analogous symmetric dynamic behaviors in the system as the radiation parameters are altered, with the order remaining constant. Finally, we implement the proposed new fractional-order HNN system on a field-programmable gate array (FPGA). The experimental results show the feasibility of the theoretical analysis.
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Affiliation(s)
- Fei Yu
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China; (Y.L.); (S.X.); (W.Y.); (Y.M.G.); (S.C.)
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Alexander P, Parastesh F, Hamarash II, Karthikeyan A, Jafari S, He S. Effect of the electromagnetic induction on a modified memristive neural map model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17849-17865. [PMID: 38052539 DOI: 10.3934/mbe.2023793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The significance of discrete neural models lies in their mathematical simplicity and computational ease. This research focuses on enhancing a neural map model by incorporating a hyperbolic tangent-based memristor. The study extensively explores the impact of magnetic induction strength on the model's dynamics, analyzing bifurcation diagrams and the presence of multistability. Moreover, the investigation extends to the collective behavior of coupled memristive neural maps with electrical, chemical, and magnetic connections. The synchronization of these coupled memristive maps is examined, revealing that chemical coupling exhibits a broader synchronization area. Additionally, diverse chimera states and cluster synchronized states are identified and discussed.
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Affiliation(s)
- Prasina Alexander
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India
| | - Fatemeh Parastesh
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Ibrahim Ismael Hamarash
- Electrical Engineering Department, Salahaddin University-Erbil, Kirkuk Rd., Erbil, Kurdistan, Iraq
- School of Computer Science and Engineering, University of Kurdistan Hewler, 40m St., Erbil, Kurdistan, Iraq
| | - Anitha Karthikeyan
- Department of Electronics and Communication Engineering, Vemu Institute of Technology, Chithoor, India
- Department of Electronics and Communications Engineering and University Centre for Research & Development, Chandigarh University, Mohali-140413, Punjab
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Shaobo He
- School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
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Mehrabbeik M, Jafari S, Perc M. Synchronization in simplicial complexes of memristive Rulkov neurons. Front Comput Neurosci 2023; 17:1248976. [PMID: 37720251 PMCID: PMC10501309 DOI: 10.3389/fncom.2023.1248976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/11/2023] [Indexed: 09/19/2023] Open
Abstract
Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure pairwise hybrid (electrical and chemical) synapses alongside a network with two-node electrical and multi-node chemical connections. The findings provide good insight into the impact of incorporating higher-order interaction in a network. Compared to two-node chemical synapses, higher-order interactions adjust the synchronization patterns to lower multi-node chemical coupling parameter values. Furthermore, the effect of altering higher-order coupling parameter value on the dynamics of neurons in the synchronization state is researched. It is also shown how increasing network size can enhance synchronization by lowering the value of coupling parameters whereby synchronization occurs. Except for complete synchronization, cluster synchronization is detected for higher electrical coupling strength values wherein the neurons are out of the completed synchronization state.
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Affiliation(s)
- Mahtab Mehrabbeik
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Alma Mater Europaea, Maribor, Slovenia
- Complexity Science Hub Vienna, Vienna, Austria
- Department of Physics, Kyung Hee University, Seoul, Republic of Korea
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Sriram S, Natiq H, Rajagopal K, Krejcar O, Krejcar O. Dynamics of a two-layer neuronal network with asymmetry in coupling. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2908-2919. [PMID: 36899564 DOI: 10.3934/mbe.2023137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.
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Affiliation(s)
- Sridevi Sriram
- Centre for Computational Biology, Chennai Institute of Technology, Chennai 600069, India
| | - Hayder Natiq
- Department of Computer Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad 10001, Iraq
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
- Institute of Technology and Business in Ceske Budejovice, Ceske Budejovice, Czechia
- Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering Technical University of Kosice, Slovakia
| | - Ondrej Krejcar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia
- School of Engineering, Monash University, Selangor, Malaysia
- College of Engineering and Science, Victoria University, Melbourne, Australia
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