1
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Yang F, Guo Q, Ma J. A neuron model with nonlinear membranes. Cogn Neurodyn 2024; 18:673-684. [PMID: 38699608 PMCID: PMC11061065 DOI: 10.1007/s11571-023-10017-5] [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: 07/11/2023] [Revised: 09/02/2023] [Accepted: 09/25/2023] [Indexed: 05/05/2024] Open
Abstract
One-layer membrane separates the gradient field in and out of the cell, while some two-layer membranes filled with excitable media/material are important to regulate the energy flow when ions are propagated and diffused. The intracellular and extracellular media can be effectively separated by the membrane. It is important to clarify and describe the biophysical function and then the capacitive property can be reproduced in equivalent neural circuit. Here, we suggest the cell membrane has certain thickness and becomes flexible under external stimuli, therefore, it is considered as a kind of nonlinear media. To mimic the physical property of the two-layer cell membrane, a nonlinear resistor is used to connect two linear circuits, which is used to describe the electrical characteristic of two sides of the cell membrane, respectively. The combination of two linear circuits via a nonlinear resistor can describe the energy characteristic and firing mode in the flexible membrane of biophysical neurons. Circuit equations are defined and converted into equivalent nonlinear oscillator like a neuron. The voltage difference for the two capacitors can be consistent with the membrane potential for the neuron. The Hamilton energy function for this neuron can be mapped from the field energy in the electronic components, and it is also derived by using Helmholtz's theorem. The neuron can show similar spiking and bursting firing patterns, and uncertain diversity in membrane potentials is effective to support continuous firing patterns and mode transition under external stimulus. Furthermore, noisy disturbance is applied to induce coherence resonance. The results indicate that the lower coefficient variability and higher average energy level supports periodic firing in the neuron under coherence resonance. Therefore, this neuron model with nonlinear membranes (or two-layer form) is more suitable for identifying the biophysical property of biological neuron.
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Affiliation(s)
- Feifei Yang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Qun Guo
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050 China
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2
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Zhang L, Xiong L, An X, Shi Q. Hamilton energy balance and synchronization behaviors of two functional neurons. Cogn Neurodyn 2023; 17:1683-1702. [PMID: 37974578 PMCID: PMC10640572 DOI: 10.1007/s11571-022-09908-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/21/2022] [Accepted: 10/28/2022] [Indexed: 11/23/2022] Open
Abstract
The nervous system is composed of various functional neurons, some of which perceive sound or light, and these physical signals can be converted into bioelectrical signals. From the biophysical point of view, piezoelectric ceramic embedded in neuronal circuits can detect the external auditory waves, while phototube can capture light signals, so as to obtain two functional neurons with auditory recognition and light-dependent recognition. Considering the two identical or different functional neurons are connected by an induction coil to stimulate magnetic field coupling, and there will be energy diversity when they are driven by different initial conditions or external stimulation. Thus, synaptic connections can be activated and awakened in an adaptive manner when field energy is exchanged, and the coupling channel remains open until the energy diversity between neurons is controlled at a limited threshold. For this purpose, a criterion of the coupling strength increases exponentially is proposed to discuss the enhancement of neuronal synaptic connections. It is found that two neurons can be coupled adaptively to achieve complete synchronization, quasi-synchronization or intermittent quasi-synchronization. These results could help in designing functional assistive devices for patients with hearing or vision impairment.
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Affiliation(s)
- Li Zhang
- School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, 730070 China
| | - Li Xiong
- School of Physics and Electromechanical Engineering, Hexi University, Zhangye, 734000 China
| | - Xinlei An
- School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, 730070 China
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China
| | - Qianqian Shi
- School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, 730070 China
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3
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Mougkogiannis P, Adamatzky A. Proteinoid Microspheres as Protoneural Networks. ACS OMEGA 2023; 8:35417-35426. [PMID: 37780014 PMCID: PMC10536103 DOI: 10.1021/acsomega.3c05670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023]
Abstract
Proteinoids, also known as thermal proteins, possess a fascinating ability to generate microspheres that exhibit electrical spikes resembling the action potentials of neurons. These spiking microspheres, referred to as protoneurons, hold the potential to assemble into proto-nanobrains. In our study, we investigate the feasibility of utilizing a promising electrochemical technique called differential pulse voltammetry (DPV) to interface with proteinoid nanobrains. We evaluate DPV's suitability by examining critical parameters such as selectivity, sensitivity, and linearity of the electrochemical responses. The research systematically explores the influence of various operational factors, including pulse width, pulse amplitude, scan rate, and scan time. Encouragingly, our findings indicate that DPV exhibits significant potential as an efficient electrochemical interface for proteinoid nanobrains. This technology opens up new avenues for developing artificial neural networks with broad applications across diverse fields of research.
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Affiliation(s)
| | - Andrew Adamatzky
- Unconventional Computing
Laboratory, UWE, Bristol BS16 1QY, U.K.
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4
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Xu M, Zhao Y, Xu G, Zhang Y, Sun S, Sun Y, Wang J, Pei R. Recent Development of Neural Microelectrodes with Dual-Mode Detection. BIOSENSORS 2022; 13:59. [PMID: 36671894 PMCID: PMC9856135 DOI: 10.3390/bios13010059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.
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Affiliation(s)
- Meng Xu
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
| | - Yuewu Zhao
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
| | - Guanghui Xu
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
| | - Yuehu Zhang
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
| | - Shengkai Sun
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
| | - Yan Sun
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
| | - Jine Wang
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
| | - Renjun Pei
- CAS Key Laboratory for Nano-Bio Interface, Division of Nano-biomedicine, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou 215123, China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, China
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5
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Ionic channel blockage in stochastic Hodgkin-Huxley neuronal model driven by multiple oscillatory signals. Cogn Neurodyn 2020; 14:569-578. [PMID: 32655717 DOI: 10.1007/s11571-020-09593-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/07/2020] [Accepted: 04/23/2020] [Indexed: 01/20/2023] Open
Abstract
Ionic channel blockage and multiple oscillatory signals play an important role in the dynamical response of pulse sequences. The effects of ionic channel blockage and ionic channel noise on the discharge behaviors are studied in Hodgkin-Huxley neuronal model with multiple oscillatory signals. It is found that bifurcation points of spontaneous discharge are altered through tuning the amplitude of multiple oscillatory signals, and the discharge cycle is changed by increasing the frequency of multiple oscillatory signals. The effects of ionic channel blockage on neural discharge behaviors indicate that the neural excitability can be suppressed by the sodium channel blockage, however, the neural excitability can be reversed by the potassium channel blockage. There is an optimal blockage ratio of potassium channel at which the electrical activity is the most regular, while the order of neural spike is disrupted by the sodium channel blockage. In addition, the frequency of spike discharge is accelerated by increasing the ionic channel noise, the firing of neuron becomes more stable if the ionic channel noise is appropriately reduced. Our results might provide new insights into the effects of ionic channel blockages, multiple oscillatory signals, and ionic channel noises on neural discharge behaviors.
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6
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Zhang G, Guo D, Wu F, Ma J. Memristive autapse involving magnetic coupling and excitatory autapse enhance firing. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.093] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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7
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Wang R, Fan Y, Wu Y. Spontaneous electromagnetic induction promotes the formation of economical neuronal network structure via self-organization process. Sci Rep 2019; 9:9698. [PMID: 31273270 PMCID: PMC6609776 DOI: 10.1038/s41598-019-46104-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 06/24/2019] [Indexed: 12/16/2022] Open
Abstract
Developed through evolution, brain neural system self-organizes into an economical and dynamic network structure with the modulation of repetitive neuronal firing activities through synaptic plasticity. These highly variable electric activities inevitably produce a spontaneous magnetic field, which also significantly modulates the dynamic neuronal behaviors in the brain. However, how this spontaneous electromagnetic induction affects the self-organization process and what is its role in the formation of an economical neuronal network still have not been reported. Here, we investigate the effects of spontaneous electromagnetic induction on the self-organization process and the topological properties of the self-organized neuronal network. We first find that spontaneous electromagnetic induction slows down the self-organization process of the neuronal network by decreasing the neuronal excitability. In addition, spontaneous electromagnetic induction can result in a more homogeneous directed-weighted network structure with lower causal relationship and less modularity which supports weaker neuronal synchronization. Furthermore, we show that spontaneous electromagnetic induction can reconfigure synaptic connections to optimize the economical connectivity pattern of self-organized neuronal networks, endowing it with enhanced local and global efficiency from the perspective of graph theory. Our results reveal the critical role of spontaneous electromagnetic induction in the formation of an economical self-organized neuronal network and are also helpful for understanding the evolution of the brain neural system.
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Affiliation(s)
- Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
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8
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Synchronous firing frequency dependence in unidirectional coupled neuronal networks with chemical synapses. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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9
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Qian Y, Zhang G, Wang Y, Yao C, Zheng Z. Winfree loop sustained oscillation in two-dimensional excitable lattices: Prediction and realization. CHAOS (WOODBURY, N.Y.) 2019; 29:073106. [PMID: 31370411 DOI: 10.1063/1.5085644] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 06/20/2019] [Indexed: 06/10/2023]
Abstract
The problem of self-sustained oscillations in excitable complex networks is the central issue under investigation, among which the prediction and the realization of self-sustained oscillations in different kinds of excitable networks are the challenging tasks. In this paper, we extensively investigate the prediction and the realization of a Winfree loop sustained oscillation (WLSO) in two-dimensional (2D) excitable lattices. By analyzing the network structure, the fundamental oscillation source structure (FOSS) of WLSO in a 2D excitable lattice is exposed explicitly. For the suitable combinations of system parameters, the Winfree loop can self-organize on the FOSS to form an oscillation source sustaining the oscillation, and these suitable parameter combinations are predicted by calculating the minimum Winfree loop length and have been further confirmed in numerical simulations. However, the FOSS cannot spontaneously offer the WLSO in 2D excitable lattices in usual cases due to the coupling bidirectionality and the symmetry properties of the lattice. A targeted protection scheme of the oscillation source is proposed by overcoming these two drawbacks. Finally, the WLSO is realized in the 2D excitable lattice successfully.
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Affiliation(s)
- Yu Qian
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Gang Zhang
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Yafeng Wang
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji 721007, China
| | - Chenggui Yao
- Department of Mathematics, Shaoxing University, Shaoxing 312000, China
| | - Zhigang Zheng
- Institute of Systems Science, Huaqiao University, Xiamen 361021, China
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10
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Rajagopal K, Parastesh F, Azarnoush H, Hatef B, Jafari S, Berec V. Spiral waves in externally excited neuronal network: Solvable model with a monotonically differentiable magnetic flux. CHAOS (WOODBURY, N.Y.) 2019; 29:043109. [PMID: 31042930 DOI: 10.1063/1.5088654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 03/21/2019] [Indexed: 06/09/2023]
Abstract
Spiral waves are particular spatiotemporal patterns connected to specific phase singularities representing topological wave dislocations or nodes of zero amplitude, witnessed in a wide range of complex systems such as neuronal networks. The appearance of these waves is linked to the network structure as well as the diffusion dynamics of its blocks. We report a novel form of the Hindmarsh-Rose neuron model utilized as a square neuronal network, showing the remarkable multistructure of dynamical patterns ranging from characteristic spiral wave domains of spatiotemporal phase coherence to regions of hyperchaos. The proposed model comprises a hyperbolic memductance function as the monotone differentiable magnetic flux. Hindmarsh-Rose neurons with an external electromagnetic excitation are considered in three different cases: no excitation, periodic excitation, and quasiperiodic excitation. We performed an extensive study of the neuronal dynamics including calculation of equilibrium points, bifurcation analysis, and Lyapunov spectrum. We have found the property of antimonotonicity in bifurcation scenarios with no excitation or periodic excitation and identified wide regions of hyperchaos in the case of quasiperiodic excitation. Furthermore, the formation and elimination of the spiral waves in each case of external excitation with respect to stimuli parameters are investigated. We have identified novel forms of Hindmarsh-Rose bursting dynamics. Our findings reveal multipartite spiral wave formations and symmetry breaking spatiotemporal dynamics of the neuronal model that may find broad practical applications.
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Affiliation(s)
| | - Fatemeh Parastesh
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran 15875-4413, Iran
| | - Hamed Azarnoush
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran 15875-4413, Iran
| | - Boshra Hatef
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran 14359-16471, Iran
| | - Sajad Jafari
- Nonlinear Systems and Applications, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Vesna Berec
- Institute of Nuclear Sciences, Vinca, PO Box 522, 11001 Belgrade, Serbia
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11
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Hindmarsh-Rose neuron model with memristors. Biosystems 2019; 178:1-9. [PMID: 30639442 DOI: 10.1016/j.biosystems.2019.01.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 12/08/2018] [Accepted: 01/08/2019] [Indexed: 11/20/2022]
Abstract
We analyze single and coupled Hindmarsh-Rose neurons in the presence of a time varying electromagnetic field which results from the exchange of ions across the membrane. Memristors are used to model the relation between magnetic flux of the electromagnetic field and the membrane potential of interacting neurons. The bifurcation analysis of Hindmarsh-Rose neurons has been carried out by varying the modulation intensity of induced current on the membrane potential. Many important dynamical behaviors such as synchrony, desynchrony, amplitude death, anti-phase oscillations, coexistence of resting and spiking state, and near death rare spikes are observed when the neurons are coupled using electrical and chemical synapses. In all cases the transverse Lyapunov exponents are plotted to observe the point of transition from desynchrony to synchrony. The memristor based analysis on neural networks can contribute to biological system modeling and can be used as a synapse in hardware of artificial neural networks.
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12
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Yuan Y, Huo H, Zhao P, Liu J, Liu J, Xing F, Fang T. Constraints of Metabolic Energy on the Number of Synaptic Connections of Neurons and the Density of Neuronal Networks. Front Comput Neurosci 2018; 12:91. [PMID: 30524259 PMCID: PMC6256250 DOI: 10.3389/fncom.2018.00091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 10/31/2018] [Indexed: 11/13/2022] Open
Abstract
Neuronal networks in the brain are the structural basis of human cognitive function, and the plasticity of neuronal networks is thought to be the principal neural mechanism underlying learning and memory. Dominated by the Hebbian theory, researchers have devoted extensive effort to studying the changes in synaptic connections between neurons. However, understanding the network topology of all synaptic connections has been neglected over the past decades. Furthermore, increasing studies indicate that synaptic activities are tightly coupled with metabolic energy, and metabolic energy is a unifying principle governing neuronal activities. Therefore, the network topology of all synaptic connections may also be governed by metabolic energy. Here, by implementing a computational model, we investigate the general synaptic organization rules for neurons and neuronal networks from the perspective of energy metabolism. We find that to maintain the energy balance of individual neurons in the proposed model, the number of synaptic connections is inversely proportional to the average of the synaptic weights. This strategy may be adopted by neurons to ensure that the ability of neurons to transmit signals matches their own energy metabolism. In addition, we find that the density of neuronal networks is also an important factor in the energy balance of neuronal networks. An abnormal increase or decrease in the network density could lead to failure of energy metabolism in the neuronal network. These rules may change our view of neuronal networks in the brain and have guiding significance for the design of neuronal network models.
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Affiliation(s)
- Ye Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Hong Huo
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Peng Zhao
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Jian Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Jiaxing Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Fu Xing
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Tao Fang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
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13
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Ge M, Xu Y, Lu L, Zhao Y, Yang L, Zhan X, Gao K, Li A, Jia Y. Effect of external periodic signals and electromagnetic radiation on autaptic regulation of neuronal firing. IET Syst Biol 2018; 12:177-184. [PMID: 33451180 DOI: 10.1049/iet-syb.2017.0069] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 03/22/2018] [Accepted: 03/25/2018] [Indexed: 01/20/2023] Open
Abstract
An improved Hindmarsh-Rose (HR) neuron model, where the memristor is a bridge between membrane potential and magnetic flux, can be used to investigate the effect of periodic signals on autaptic regulation of neurons under electromagnetic radiation. Based on the improved HR model driven by periodic high-low-frequency current and electromagnetic radiation, the responses of electrical autaptic regulation with diverse high-low-frequency signals are investigated using bifurcation analysis. It is found that the electrical modes of neurons are determined by the selecting parameters of both periodic high and low-frequency current and electromagnetic radiation, and the Hamiltonian energy depends on the neuronal firing modes. The effects of Gaussian white noise on the membrane potential are discussed using numerical simulations. It is demonstrated that external high-low-frequency stimulus plays a significant role in the autaptic regulation of neural firing mode, and the electrical mode of neurons can be affected by the angular frequency of both high-low-frequency forcing current and electromagnetic radiation. The mechanism of neuronal firing regulated by high-low-frequency signal and electromagnetic radiation discussed here could be applied to research neuronal networks and synchronisation modes.
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Affiliation(s)
- Mengyan Ge
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Ying Xu
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Lulu Lu
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Yunjie Zhao
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Lijian Yang
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Xuan Zhan
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Kaifu Gao
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Anbang Li
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
| | - Ya Jia
- Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, 430079, People's Republic of China
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