Article
Mathematics
Sergey V. Stasenko, Alexey N. Mikhaylov, Victor B. Kazantsev
Summary: This study investigates an unstructured neuron network model consisting of excitatory and inhibitory neurons, and explores the application of memristors with spike timing-dependent plasticity (STDP) characteristics in the network. The findings reveal that memristor-based STDP for inhibitory connections can suppress bursting dynamics and induce random spiking mode in the network. These results contribute to the advancement of invasive neurointerfaces and the understanding and control of epileptiform activity.
Article
Mathematics
Sergey V. V. Stasenko, Victor B. B. Kazantsev
Summary: We propose a mathematical model of a spiking neural network that interacts with the brain extracellular matrix (ECM). The model shows that ECM-mediated regulation of neuronal activity promotes the formation of population bursts. We investigate how varying the strength of ECM influence on synaptic transmission affects spiking dynamics and neuronal population synchrony.
Article
Physics, Multidisciplinary
Sergey V. Stasenko, Victor B. Kazantsev
Summary: We investigated how a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes can represent information content in the form of two-dimensional images. The SNN includes excitatory and inhibitory neurons, while the astrocytes provide slow modulation of synaptic transmission strength. We found that astrocytic modulation prevents hyperexcitation and non-periodic bursting activity, allowing the restoration of the image supplied during stimulation.
Article
Engineering, Electrical & Electronic
Huiyuan Liu, Xiaojian Zhu, Zhecheng Guo, Ri He, Xinze Li, Qihao Sun, Xiaoyu Ye, Cui Sun, Yu Tian, Run-Wei Li
Summary: This study presents a memristor that can mimic the burst-firing features of biological neurons by generating periodic voltage oscillation groups. The burst frequency can be adjusted and used for burst frequency coding. The artificial neural system based on bursting neurons achieves high recognition accuracy for Fashion-MNIST tasks.
ACS APPLIED ELECTRONIC MATERIALS
(2023)
Article
Neurosciences
Changsheng Qi, Yuye Li, Huaguang Gu, Yongxia Yang
Summary: Excitatory modulation reduces the number of spikes and firing rate, while inhibitory modulation increases both indicators. The fast autaptic current enhances the amplitude of the action potential and lengthens the interspike interval, while the slow autaptic current plays a role following the peak of action potential. This study extends the understanding of neurodynamics and presents potential functions of the fast autapse.
COGNITIVE NEURODYNAMICS
(2023)
Article
Physics, Multidisciplinary
Ben Cao, Huaguang Gu, Yuye Li
Summary: This study investigates two different bursting patterns and finds that excitatory modulation can reduce the number of spikes per burst under suitable time delay and self-feedback strength. The average firing frequency of the bursting patterns shows different trends, which are related to their internal mechanisms.
Article
Mathematics, Interdisciplinary Applications
Sajedeh Aghababaei, Sundarambal Balaraman, Karthikeyan Rajagopal, Fatemeh Parastesh, Shirin Panahi, Sajad Jafari
Summary: This paper investigates the influence of autaptic connections on chimera states, showing that the occurrence domain of chimeras is affected differently by coupling strength and autapse parameters. By adjusting the coupling strength and autapse parameters, an ideal dynamical state can be achieved.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Bo Lu, Huaguang Gu, Xianjun Wang, Hongtao Hua
Summary: This paper investigates paradoxical phenomena in bursting activity modulated by inhibitory autapse, revealing that with increased inhibitory autaptic conductance, burst duration widens or spike number per burst increases. Different bursting patterns are observed, and the paradoxical enhancement of bursting activity is explained by nonlinear dynamics.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Engineering, Electrical & Electronic
Weiwei Fan, Xiongjian Chen, Huagan Wu, Ze Li, Quan Xu
Summary: This paper investigates the dynamics of a 3D Morris-Lecar neuron model with a memristive autapse and finds that the memristive autapse can change the firing period and dynamics of the neuron. It is also discovered that increasing the autapse time delay expands the chaotic regions and leads to the emergence of multistability. Additionally, the study explores the synchronization of coupled memristive autaptic neurons and shows that considering the memristive autapse enhances the synchronization of chaotic neurons, with higher autapse gain reducing the required coupling strength for synchronization.
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2023)
Article
Physics, Multidisciplinary
Ping Zhou, Ying Xu, Jun Ma
Summary: Photoelectric neurons can perceive external illumination and generate output voltage and/or current by absorbing some external energy. During photoelectric conversion, external illumination is filtered and transformed into equivalent electric stimulus. Visual neurons have intrinsic self-adaptation and exhibit appropriate firing patterns under specific frequency band of external illumination. Therefore, light-sensitive neurons developed from photocurrent-driven neural circuits can be used to study signal processing in visual neurons.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
Ying Xu, Jun Ma
Summary: Temperature influences ion channel activation and neuronal excitability, affecting neural activity modes. Autapse connections enhance neuron adaptability to stimuli, controlling firing patterns. Adjusting autapse intensity and time delay can control neural activity, balancing and enhancing temperature effects.
Article
Engineering, Mechanical
Yongxia Yang, Yuye Li, Huaguang Gu, Changsheng Qi
Summary: This paper investigates the opposite roles of inhibitory autapses with fast and slow time scales on modulating bursting activities in theoretical models, providing a novel viewpoint on inhibitory autapse and bursting in brain neurons. The results show that fast and slow inhibitory autapses induce enhancement and reduction of bursting activities respectively, and the underlying bifurcation mechanisms and dynamics of autaptic current are acquired.
NONLINEAR DYNAMICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Xianjun Wang, Huaguang Gu, Yanbing Jia
Summary: Recent studies on neurodynamics have focused on paradoxical phenomena where inhibitory modulations enhance neuronal firing activity or excitatory modulations reduce firing activity. In this paper, the authors identify that fast and slow excitatory autapses induce opposite responses in a neuronal model. The fast decay of the excitatory autapse is found to be the essential factor for reducing bursting activity, while the slow decay of the autapse leads to increased bursting activity.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Zhigang Zhu, Xiaofeng Zhang, Yisen Wang, Jun Ma
Summary: This paper investigates the adaptation and filtering capability of a sound-sensitive neural circuit using a FitzHugh-Nagumo based autaptic neuron. The results show that the excitatory chemical autapse can act as a narrow-band filter, while the inhibitory chemical autapse enables the neuron to converge its amplitude and exhibits bursting adaptation. Additionally, the electrical autaptic neuron's response can be modulated by both time-delay feedback gains.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2023)
Article
Mathematics, Interdisciplinary Applications
O. Shchur, A. Vidybida
Summary: This paper analytically studies the impact of inhibitory autapse on neuronal activity, using a set of non-adaptive spiking neuron models with delayed feedback inhibition. The probability density function of output interspike intervals (ISIs) is found exactly, allowing for an accurate description of neuronal activity. The results are applied to a subset of neuronal models with a specific distribution of input intervals, leading to the identification of a model-independent initial part of ISIs PDF.
FLUCTUATION AND NOISE LETTERS
(2023)
Article
Engineering, Multidisciplinary
Ying Xie, Ping Zhou, Jun Ma
Summary: This paper investigates the problem of information exchange and signal encoding between neural circuits. The results show that adaptive synchronization between different neural circuits can be achieved through coupling, and energy diversity plays an important role in controlling synchronization.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Multidisciplinary
Ying Xie, Zhao Yao, Jun Ma
Summary: The static distribution of ions within cells can affect the spatial distribution of electric fields, while the diffusion and propagation of ions can result in channel currents accompanied by magnetic fields. The energy within cells and neurons can be altered by external stimuli or shape deformation. Energy pumping and adaptive synaptic connections maintain energy balance in clustered networks. Heterogeneity arises in synchronous and homogeneous networks due to asymmetric energy transport, and adaptive synaptic regulation occurs to achieve local energy balance. External stimuli of varying intensity induce energy diversity and shape deformation, leading to the development of local heterogeneity.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2023)
Article
Physics, Condensed Matter
Feifei Yang, Xikui Hu, Guodong Ren, Jun Ma
Summary: A simple neural circuit coupled by magnetic flux-controlled memristor is used to describe the electromagnetic effect and radiation on biological neurons. The effect of external electric field on biophysical neurons is identified by adding a charge-controlled memristor into a nonlinear circuit. The firing patterns of the memristive circuit can be adjusted by tuning the angular frequency of an external voltage source, and the physical field energy and equivalent Hamilton energy are dependent on the firing modes of neural activities. The exchange and propagation of field energy in clustered neurons is achieved by regulating the charge flow, and coupling intensity is controlled by the energy difference between adjacent neurons for perfect energy balance and saturation.
EUROPEAN PHYSICAL JOURNAL B
(2023)
Article
Computer Science, Artificial Intelligence
Yong Wang, Mingxing Gao, Xun Ran, Jun Ma, Leo Yu Zhang
Summary: Matrix factorization (MF) is a popular technique in recommendation systems (RSs). However, the privacy protection of item sets rated by users is often ignored in existing privacy-preserving MF schemes. To address this issue, a strategy based on piecewise mechanism (PM) is proposed to protect both rating values and item sets. An improved MF based on PM (IMFPM) is introduced, which divides item profiles into global and personal information and utilizes random projection technology to reduce privacy noise. Theoretical analysis and experiments show that IMFPM provides strong privacy protection and high prediction quality, making it a promising scheme for distributed recommendation systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Zhigang Zhu, Xiaofeng Zhang, Yisen Wang, Jun Ma
Summary: This paper investigates the adaptation and filtering capability of a sound-sensitive neural circuit using a FitzHugh-Nagumo based autaptic neuron. The results show that the excitatory chemical autapse can act as a narrow-band filter, while the inhibitory chemical autapse enables the neuron to converge its amplitude and exhibits bursting adaptation. Additionally, the electrical autaptic neuron's response can be modulated by both time-delay feedback gains.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2023)
Article
Mathematics, Applied
Fuqiang Wu, Yitong Guo, Jun Ma, Wuyin Jin
Summary: In this paper, an equivalent circuit is designed using two memristive Josephson junctions coupled by a resistor and two inductors to mimic the characteristics of biological neurons. The circuit can reproduce bursting activities similar to neurons through numerical simulation. Additionally, the coupled memristive Josephson junctions can achieve in-phase and antiphase synchronization. The synchronous behaviors are analyzed by discerning the interspike interval of phase difference and average Hamilton energy.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Mathematics, Applied
Veli Baysal, Ramazan Solmaz, Jun Ma
Summary: The signal encoding capacity of the nervous system is significantly related to the spiking regime of neurons. Chaotic fluctuations in the neuronal medium are thought to enhance cognitive functions. External chaotic activity at a suitable level enhances the weak signal encoding performance of neurons, especially when the weak signal frequency matches the chaotic fluctuations-induced sub-threshold oscillations frequencies. This manipulation is explained by chaotic resonance.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Han Bao, Mengjie Hua, Jun Ma, Mo Chen, Bocheng Bao
Summary: In this article, a Memristor synapse with activated synaptic plasticity is introduced as an adaptive connection synaptic weight. An improved Hopfield neural network with two memristive self-connection synaptic weights is presented to demonstrate its kinetic effects. The stability distributions of the network are analyzed by analyzing the two nonzero roots of the eigenvalue polynomial. Bifurcation behaviors and phase portraits are used to investigate the parameter-related behaviors. Furthermore, the kinetic effects of memristor synapses are demonstrated by taking the memristor initial conditions as two invariant measures.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Physics, Multidisciplinary
Feifei Yang, Jun Ma
Summary: In this study, a neural circuit was rebuilt using a thermistor and a photocell, making it sensitive to external temperature and illumination. A magnetic flux-controlled memristor was used to connect the neural circuits, with its coupling channel adaptively controlled by energy diversity. The involvement of memristive synapses activated the ability for energy pumping and storage via magnetic field, allowing for controlled energy propagation along the memristive channel. It was found that by enhancing the magnetic field coupling via memristor, neurons could achieve complete synchronization and energy balance.
PRAMANA-JOURNAL OF PHYSICS
(2023)
Article
Physics, Multidisciplinary
F. Yang, Y. Wang, J. Ma
Summary: Due to the diversity in excitability and intrinsic parameters, neurons present different firing patterns and energy diversity. In this work, four FHN neurons in chain and ring networks are connected via memristive synapses, and their collective activities are controlled by adaptively taming the memristive coupling. Bifurcation analysis, Lyapunov exponent spectrum and Hamilton energy are calculated to investigate the dynamics dependence on external stimulus and parameters.
INDIAN JOURNAL OF PHYSICS
(2023)
Review
Engineering, Multidisciplinary
Jun Ma
Summary: Diffusion of ions inside and outside cells leads to a gradient electromagnetic field that regulates membrane potential. External stimuli inject energy to disrupt the energy balance between the magnetic and electric fields in a cell. Activation of biophysical functions and self-adaptation of biological neurons depend on energy flow, and synapse connection is controlled to achieve energy balance. When more neurons are clustered together, field energy is exchanged and shape formation occurs to achieve local energy balance, preventing bursting synchronization and seizure. This review presents various biophysical neuron models and explains their physical aspects, clarifying the controllability of functional synapses, formation of heterogeneity, and defects to understand synchronization stability and cooperation between functional regions. These models and findings provide new insights into nonlinear physics and computational neuroscience.
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A
(2023)
Article
Mathematics, Applied
Ya Wang, Liang Wang, Huawei Fan, Jun Ma, Hui Cao, Xingang Wang
Summary: Recent in vivo experiments have shown that astrocytes, a type of glial cell previously thought to provide structural and metabolic support to neurons, actively participate in brain functions by regulating neural firing activities. In this study, the authors propose a complex neuron-astrocyte network model and investigate the role of astrocytes in regulating cluster synchronization behaviors of chaotic neurons. They find that a specific set of neurons form a synchronized cluster while the remaining neurons remain desynchronized. Moreover, the cluster switches between synchronous and asynchronous states in an intermittent fashion, known as the breathing cluster phenomenon. The authors conduct theoretical investigations on the synchronizability of the cluster and reveal that the cluster contents are determined by network symmetry, while the breathing of the cluster is attributed to the interplay between the neural network and the astrocyte.
Article
Computer Science, Artificial Intelligence
D. Vignesh, Jun Ma, Santo Banerjee
Summary: In this article, a discrete fractional Hopfield neural network model is proposed to investigate the influence of external stimuli in the presence of electromagnetic induction and radiation. The network model exhibits chaotic dynamics and coexisting behavior. The study also explores the generation of multi-scroll attractors by varying the level of the pulse function introduced to electromagnetic induction. The findings contribute to our understanding of discrete fractional memristors and shed light on the dynamical behavior of neurons and their electrical activity in the brain.
Article
Computer Science, Artificial Intelligence
Fuqiang Wu, Hao Meng, Jun Ma
Summary: Employing electronic components such as memristor and Josephson junction to imitate biological neurons and synapses has been a popular research topic in recent years. This paper revisits a previous work on memristive Josephson junction and proposes a new model called inductive memristive Josephson junction (L-MJJ) by adding an inductor with internal resistor. The L-MJJ model can reproduce the square-wave bursting behavior of classical neuronal models and achieve bursting synchronization similar to nonlinear coupling neurons. This research aims to build a bridge between superconducting physics and theoretical neuroscience, and demonstrates the potential feasibility of using this junction in designing neuron-inspired computations for larger-scale neuromorphic networks.
Article
Mathematics, Applied
Jun Ma
Summary: Continuous dynamical systems with different nonlinear terms can exhibit rich dynamical characteristics. In this study, reliable algorithms for discretization and equivalent maps are proposed to reproduce similar dynamics as the nonlinear oscillators. The application of scale transformation helps convert maps into equivalent nonlinear oscillators and define energy function. Additionally, a generic memristive term is introduced into the map, which can be used to develop memristive oscillators and clarify the energy function and oscillatory characteristics.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
