作者：
Ding, Qianming;Wu, Yong;Huang, Weifang;Jia, Ya
期刊：
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS,2024年:1-14 ISSN：1951-6355
通讯作者：
Jia, Y
作者机构：
[Wu, Yong; Huang, Weifang; Jia, Ya; Ding, Qianming] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
摘要：
The mathematical optimization techniques may control the network to target firing patterns by adjusting the weights of network nodes. Inspired by the dynamics of dynamical learning, we recently proposed a technique for dynamic learning of synchronous (DLS) to control the firing state of neural networks. In this study, we apply the DLS technique to a Hodgkin-Huxley-style neural network, and investigate in regular, random, small-world and scale-free networks. We use the DLS technique to accomplish the network adaptive global synchronization, adaptive local synchronization, and phase locking with a single supervisory node. Furthermore, we investigated the robustness of the DLS technique in noisy environments and find that the DLS technique demonstrates remarkable effectiveness even in the presence of weak noise. However, in scenarios with stronger noise, there is a trade-off between optimizing training and avoiding overfitting, i.e., a too narrow weight adjustment range may hinder training effectiveness, while an excessively wide range results in abnormal node firing dynamics. We expect the DLS technique to be potentially valuable for more studies of nonlinear systems.
摘要：
Dynamical rewiring widely exists in complex systems, however the impact of dynamical rewiring in the synchronization of neural systems is currently unknown. In this paper, we use memristive FitzHugh-Nagumo neurons to construct random, small-world and scale-free networks in which the connections between neurons can be rewired, and investigate the influence of rewiring on the synchronization of neural networks in with/without Gaussian white noise, and comparing it to the corresponding static networks. We found that dynamical rewiring enhances the synchronization of the network, and the degree of synchronization will be higher when the rewiring period is shorter and the rewiring proportion is larger. In addition, the synchronization of the network gradually diminishes as the coupling strength decreases and the noise intensity increases, and rewiring networks always exhibit superior synchronization to static networks since the dynamical rewiring enhances the interaction between neurons. Our study shows that neural network models with dynamically changing topology are more suitable and realistic network models, which may reveal the profound significance of dynamic rewiring for the multifaceted dynamic flexibility and adaptability of neural systems.
摘要：
Optogenetics as an emerging technology can eliminate spiral waves in myocardial tissue. The heat generated during illumination of myocardial tissue is an overlooked influence. Even small fluctuations in temperature may affect the action potentials of cardiomyocyte. In this paper, a minimal ventricular model and a simplified model of optogenetics are employed to study the effects of heat generation by illumination on elimination of spiral waves. The Luo-Rudy model and Channelrhodospin-2 light-sensitive ion channel model are used to validate our conclusions. We induce drift of spiral waves through inhomogeneities generated by discrete gradients of illumination. The inhomogeneity of temperature caused by gradient illumination can inhibit the elimination of spiral waves. Spiral waves in the myocardial medium can be induced to drift more efficiently by controlling temperature changes in the myocardial medium during illumination. We emphasized the importance of temperature factors in optogenetic experiments, hoping that our results could provide guidance for its clinical applications.
作者机构：
[Wu, Yong; Fu, Ziying; Yang, Lijian; Jia, Ya; Li, Tianyu; Zhan, Xuan; Yu, Dong; Ding, Qianming] Cent China Normal Univ, Inst Biophys, Wuhan, Peoples R China.;[Wu, Yong; Yang, Lijian; Jia, Ya; Li, Tianyu; Zhan, Xuan; Yu, Dong; Ding, Qianming] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.;[Fu, Ziying] Cent China Normal Univ, Sch Life Sci, Wuhan, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Inst Biophys, Wuhan, Peoples R China.;Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan, Peoples R China.
摘要：
Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM. The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. To further understand the relevance of structure and function in brain neural networks, we explored how WM activity duration is affected by network structure in a WM task function. Firstly, we constructed a spiking neural network and found a dependence of WM activity duration on synaptic currents. This dependence is consistent with the recent experimental observation of a gradient in receptor expression along the WM gradient network. Second, we performed the WM task independently by generating different randomized networks. It was found that network structure can be a key factor in separating persistent and non-persistent activities during the delay period. Over-expression of structures representing information transmission and cycle contributes to the maintenance of WM activity. Finally, in conjunction with anatomical data, we modeled the duration of WM activity in different brain regions. We suggest that WM-related activity relies on interactions between local and distributed networks.
摘要：
Synchronous firing of neurons in neural networks is of great physiological significance. Based on improved Hodgkin-Huxley model with electromagnetic induction, a modular neural network consisting of two small-world networks unidirectionally coupled is studied. By utilizing dynamic learning of synchronization (DLS) technique, it is shown that both synchronization and chimera states of modular neural networks can be achieved by dynamic adjustment of electromagnetic induction strength. Therefore, the generation of synchronized states can be controlled. By adjusting the electromagnetic induction strengths of all neurons in the network as a whole and in clusters, the global synchronization and cluster synchronization modes of the network are achieved, respectively. The chimera state is occurred by regulating the electromagnetic induction strength of part of the neurons in networks through DLS. When the first subnetwork is the chimera state mode, increasing the number of synchronized neurons makes the synchronization of second subnetwork more stable. The robustness of the DLS regulated synchronization mode is also demonstrated under different noise intensities and network topology. Our results showed that the DLS technique is highly effective in adjusting electromagnetic induction parameters to improve the synchronization of complex neural networks, which may provide new insights for various neural networks to transmit physiological information by utilizing synchronization state.
摘要：
The functional neurons are basic building blocks of the nervous system and are responsible for transmitting information between different parts of the body. However, it is less known about the interaction between the neuron and the field. In this work, we propose a novel functional neuron by introducing a flux-controlled memristor into the FitzHugh-Nagumo neuron model, and the field effect is estimated by the memristor. We investigate the dynamics and energy characteristics of the neuron, and the stochastic resonance is also considered by applying the additive Gaussian noise. The intrinsic energy of the neuron is enlarged after introducing the memristor. Moreover, the energy of the periodic oscillation is larger than that of the adjacent chaotic oscillation with the changing of memristor-related parameters, and same results is obtained by varying stimuli-related parameters. In addition, the energy is proved to be another effective method to estimate stochastic resonance and inverse stochastic resonance. Furthermore, the analog implementation is achieved for the physical realization of the neuron. These results shed lights on the understanding of the firing mechanism for neurons detecting electromagnetic field.
摘要：
The energy consumption in synapses has been an issue of great concern, and we apply the resistor to function as the electrical synapse, and the memristor is utilized as the chemical synapse. We investigate the dynamics of neurons during the synchronization, as well as the energy consumption associated with coupled channels. The numerical results revealed that two neurons with different initial values can get into a complete synchronization, regardless of whether they are coupled via resistive or memristive synapse. Furthermore, the energy consumption of electrical synaptic coupling is companied by an energy phase transition burst that causes a sharp increase or decrease in the channel energy consumption. In contrast, memristive coupling promotes steady information and energy exchange between neurons, with relatively lower energy consumption compared to electrical synaptic coupling. Meanwhile, the memconductance of the memristor is lower than that of resistive coupling, leading to a lower coupling strength when the synchronization is achieved. Furthermore, the employment of the energy switch to trigger synaptic activation or silence can effectively control synaptic activity, and neurons can spontaneously synchronize and maintain energy balance. The energy difference between chaotic neurons can cause the transitions between synchronization, desynchronization, and resynchronization. The findings may provide insights for developing genuine neural circuits and leveraging them in artificial neuron design.
摘要：
Behaviors and auditory physiological responses of some species of echolocating bats remain unaffected after exposure to intense noise, but information on the underlying mechanisms remains limited. Here, we studied whether the vocalization-induced middle ear muscle (MEM) contractions (MEM reflex) and auditory fovea contributed to the unimpaired auditory sensitivity of constant frequency-frequency modulation (CF-FM) bats after exposure to broad-band intense noise. The vocalizations of the CF-FM bat, Hipposideros pratti, were inhibited through anesthesia to eliminate the vocalization-induced MEM reflex. First, the anesthetized bats were exposed to intense broad-band noise, and the findings showed that the bats could still maintain their auditory sensitivities. However, auditory sensitivities were seriously impaired in CBA/Ca mice exposed to intense noise under anesthesia. This indicated that the unimpaired auditory sensitivity in H. pratti after exposure to intense noise under anesthesia was not due to anesthetization. The bats were further exposed to low-frequency band-limited noise, whose passband did not overlap with echolocation call frequencies. The results showed that the auditory responses to sound frequencies within the noise spectrum and one-half octave higher than the spectrum were also unimpaired. Taken together, the results indicate that both vocalization-induced MEM reflex and auditory fovea do not contribute to the unimpaired auditory sensitivity in H. pratti after exposure to intense noise. The possible mechanisms underlying the unimpaired auditory sensitivity after echolocating bats were exposed to intense noise are discussed.
摘要：
Spike-timing-dependent plasticity (STDP) is one of the important rules for the change of synaptic weights between neurons in biological nervous systems. In this paper, we study the effect of STDP on the synchronization phenomenon induced by time delay in the neuronal network which is the scale-free network with small-world property, and nodes of the network are constructed by Izhikevich neuron and connected by chemical synapses. For appropriate time delay values, there exists an optimal range of STDP maximum weight value in which the synchronization of the network is better, and in addition the synchronization is decreased with the increasing of STDP maximum weight value. The network with high synchronization has a centralized distribution of synaptic weights within it, while conversely, an unsynchronized network has a more discrete distribution of synaptic weights. When the STDP maximum weight value is too small, the collective firing pattern of network is not affected by synaptic current, and the synchronization of the network is also not affected. Interestingly, comparing with the small-world network and the scale-free network, it is found that a network has a smaller range of optimal STDP maximum weight values when the network is of larger average clustering coefficient, shorter average shortest path length, and higher small-world property. Our results can illuminate the potential significance of STDP for information processing and transmission in the nervous system.
作者机构：
[Wu, Yong; Jia, Ya; Li, Tianyu; Yu, Dong; Ding, Qianming] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Biophys, Wuhan 430079, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
摘要：
The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.
摘要：
In recent years, the coexistence of different states in the neural system has attracted widespread interest. Researchers have found a coexisting state of spiking and resting in homogeneous networks, which is known as the chimera-like state. The real cortical network is a much more complex and heterogeneous network. Therefore, the excitatory-inhibitory cortical neuronal network is constructed based on Hodgkin-Huxley neuronal model in this paper, and the chimera-like state is further investigated in the heterogeneous network. It is found that the chimera-like state is related to the balance between excitatory and inhibitory synaptic currents. The excitatory coupling current can counteract the initial condition effect and promote synchronized firing of neurons in the network. The inhibitory coupling current desynchronizes the network and thus induces synaptic noise, resulting in an inverse bell-shaped dependence of the change in the number of spiking neurons. We analyzed the underlying mechanisms of synaptic noise in the phase plane diagram and found it has asymmetry for the neuronal state transition. In addition, neurons with low degrees have a higher probability of undergoing state transitions. Finally, we verified that the chimera-like state is robust to network topology and initial conditions. The results provide a new insight into neuronal interactions in heterogeneous networks and might help to reveal the mechanisms of coexistence of different states in the cortical network.
摘要：
Understanding and controlling neural network synchronization is crucial for neuroscience in revealing brain functions and addressing neurological disorders. This study explores the innovative use of dynamic learning of synchronization (DLS) technology to enhance synchronization within neuronal networks. Using the Hodgkin-Huxley model across various network topologies, including Erdős-Rényi random graphs, small-world, and scale-free networks, it dynamically adjusts external electrical excitation to study its effects on network synchrony. To further demonstrate the universality of DLS technology, this study also validates the main results using larger-scale networks and the Izhikevich and FitzHugh-Nagumo models. The research quantifies the enhancement of synchrony through DLS, using root-mean-square error (RMSE) and synchronization factors as metrics. Findings show that DLS effectively boosts network synchrony by dynamically adjusting external excitation in response to node differences, significantly in both small-world and scale-free networks, irrespective of synaptic connections. Furthermore, DLS demonstrates potential for targeted synchronization enhancement in specific region of network. This paper highlights DLS technology's effectiveness in modulating external excitation to improve complex neural network synchrony, providing new insights into neural synchronization and information transmission.
作者：
Li, Tianyu;Yu, Dong;Wu, Yong;Ding, Qianming;Jia, Ya
期刊：
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS,2024年233(4):797-806 ISSN：1951-6355
通讯作者：
Jia, Y
作者机构：
[Wu, Yong; Jia, Ya; Li, Tianyu; Yu, Dong; Ding, Qianming] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
关键词：
EPJ Special Topics;publication;topical issues;journal;EPJ
摘要：
Transmission of weak signals in neural networks is crucial for understanding the functionality of brain. In this work, stochastic resonance (SR) in the three neuron FitzHugh–Nagumo (FHN) motifs and its small-world network with higher order motif interactions are studied. Simulation results show that a single motif induces SR and responds better to high-frequency weak signal. Stronger coupling strength within the motif increases the firing rate of the output neurons, resulting in a more pronounced resonance. Considering only the connections within the motif, a higher in-degree of the output neuron or a shorter minimum path length between input and output neurons will lead to a better response to weak signals. SR phenomena can also be observed in small-world networks composed of these motif. Increasing whether the motif coupling or node coupling strength enhances the firing rate of output neurons, amplifying the response. There is a very strong correlation between firing rate of output neurons and response. Our results may provide insights into the propagation of weak signals in higher order networks and the selection of appropriate network topology.
摘要：
The phenomenon in which the response of a neuronal network to a weak signal is significantly enhanced in moderate noise is known as stochastic resonance (SR). Most of the previous studies on the transmission of signals by networks have been based on static synaptic connections, whereas dynamic synaptic connections modified by spike-time-dependent plasticity (STDP) are the basis of learning and memory in the nervous system. In this paper, we explore the phenomenon of SR in a neuronal network consisting of different ratios of excitatory vertebral neurons and inhibitory interneurons. The equivalent circuit method was employed to assess the average energy efficiency of the network. The differences in signal response before and after the introduction of STDP were compared for purely excitatory, purely inhibitory and excitatory-inhibitory networks, respectively. It was found that excitatory STDP promotes the network's response to weak signals, while inhibitory STDP has the opposite effect. The introduction of the inhibitory STDP makes the inhibitory network insensitive to the modulation of the coupling strength and increases its robustness. Furthermore, in the excitatory-inhibitory network, we found that STDP had little effect on the overall signalling of the network, and that the network's response to weak signals was more stable. Our findings contribute to the understanding of the importance of excitatory-inhibitory balance in ensuring accurate transmission and processing of information and provide new insights into the role of STDP in neuronal information processing.
摘要：
Most external stimuli, including sound, temperature, and illumination, exhibit spatially heterogeneous, and different amplitudes of the same signal are received by neurons at different positions in the neural network. To address this issue, we constructed a grid-like neural network using memristive FitzHugh-Nagumo neurons. The neuronal responses depend on the spatially distributed stimuli, with the stimulus amplitudes being determined by the distance from the central area. Consequently, complete synchronization occurs in the network comprising periodic neurons, chaotic neurons, and their hybrid forms. Periodic patterns maintain the highest Hamilton energy whereas the lowest Hamilton energy appears in chaotic neurons. In a network consisting of chaotic neurons, the synchronization threshold is larger compared to the other types. In particular, the periodic neurons with the highest energy oscillations can regulate the low-energy chaotic neurons into periodic patterns. Similar conclusions are drawn in a chain-like network. The results advance the understanding of the synchronization mechanisms in the presence of spatial heterogeneity.
作者机构：
[Wu, Yong; Huang, Weifang; Yang, Lijian; Jia, Ya; Ding, Qianming] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.;[Fu, Ziying] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
摘要：
Electrical signal propagation in multi-layer neural networks plays a crucial physiological role. This study constructs a three-layer neural network to simulate the orderly propagation of signals between senses, exploring its neural basis. Comprising input, learning, and output layers, the network employs a voltage-based spike-timing- dependent plasticity (STDP) rule to control synaptic interactions, aiming to learn functional relationships to facilitate signal propagation. Research indicates the network can effectively transmit signals under suitable conditions, whether or not there are synaptic connections between the input and output layers. Statistical analysis shows that learning effects are more pronounced without input-output connection interference. Further scalability tests reveal that learning stability is maintained in large-scale networks without these connections; however, their introduction imposes stricter conditions for successful learning as network size increases. This study offers insights into how neural networks can mimic complex biological signal processing tasks.
摘要：
The simplicial contagion model is employed to study the spreads of two epidemics with mutation in high-order networks. The original epidemic can give birth to a mutated epidemic, but not vice versa. Numerical simulations and mean-field theory results reveal that the spread of the mutated epidemic is entirely dependent on the original epidemic if it cannot spread independently. Conversely, the spread of the original epidemic is entirely inhibited when mutated epidemic spreads by itself. The stability analysis of mean-field theory explains the extinction of the original epidemic and the emergence of a bistable region. Two stable equilibrium points remain unchanged despite variations in parameters like the original epidemic's infection probabilities and mutation rates. While the neighborhood of the stable equilibrium points is regulated by the above parameters. Our conclusions have also been validated in real-world networks.
作者机构：
[Wang, Xueqin; Liu, Chaoyue; Xie, Ying; Jia, Ya; Li, Tianyu; Yu, Dong] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
通讯机构：
[Jia, Y ] C;Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
关键词：
Two-compartment neuron;Chemical autapse;Gaussian white noise;Inverse stochastic resonance
摘要：
Inverse stochastic resonance (ISR) is a depression phenomenon of firing activity of neuron with respect to noise, and the characteristics of neural systems are determined by neuronal morphology. The effects of neuronal morphology on ISR remain unknown. Here, ISR effect in two-compartment neuron model is investigated. It is found that the neuronal morphology influences the number of stable states of neuronal discharge, thereby governs ISR effect. The ISR effect appears within the parameters region of bistable state, which is consistent with prior experimental and theoretical findings. With the increasing of time delay, average firing rate of neuron exhibits multiple local minima, which is known as multiple ISR. Time delay and coupling strength affect neuronal firing pattern by altering phase of stimulus and strength of autaptic current. These results may provide a novel perspective on ISR effect in nervous system.
摘要：
The environment noise may disturb animal behavior and echolocation via three potential mechanisms: acoustic masking, reduced attention and noise avoidance. Compared with the mechanisms of reduced attention and noise avoidance, acoustic masking is thought to occur only when the signal and background noise overlap spectrally and temporally. In this study, we investigated the effects of spectrally non-overlapping noise on echolocation pulses and electrophysiological responses of a constant frequency-frequency modulation (CF-FM) bat, Hipposideros pratti. We found that H. pratti called at higher intensities while keeping the CFs of their echolocation pulses consistent. Electrophysiological tests indicated that the noise could decrease auditory sensitivity and sharp intensity tuning, suggesting that spectrally non-overlapping noise imparts an acoustic masking effect. Because anthropogenic noises are usually concentrated at low frequencies and are spectrally non-overlapping with the bat's echolocation pulses, our results provide further evidence of negative consequences of anthropogenic noise. On this basis, we sound a warning against noise in the foraging habitats of echolocating bats. Hipposideros pratti called at higher intensities while keeping the constant frequencies of their echolocation pulses consistent under spectrally non-overlapping background noise conditions. The noise could decrease auditory sensitivity and sharp intensity tuning, suggesting an acoustic masking effect. These results provide further evidence of negative consequences of anthropogenic noise.
