检索结果-内蒙古大学图书馆

Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units

Physical Review E 2024年第1期110卷 014130-014130页

作者： Joaquin Almeira Tomas S. Grigera Daniel A. Martin Dante R. Chialvo Sergio A. Cannas Instituto de Física Enrique Gaviola (IFEG-CONICET) Facultad de Matemática Astronomía Física y Computación Universidad Nacional de Córdoba 5000 Córdoba Argentina 1425 Buenos Aires Argentina Facultad de Matemática Astronomía Física y Computación CONICET and Departamento de Física Facultad de Ciencias Exactas Consiglio Nazionale delle Ricerche 00185 Rome Italy Instituto de Ciencias Físicas (ICIFI-CONICET) Center for Complex Systems and Brain Sciences (CEMSC3) Escuela de Ciencia y Tecnología

We present a mean-field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyze the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first-order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units f is smaller than some critical value ft≲1/2, even for a finite system. Moreover, any solution for f<1/2 can be mapped to a solution for purely excitatory systems (f=0). In finite systems, when the system is dominated by inhibition (f>ft), the first-order transition is replaced by a pseudocritical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behavior of a continuous phase transition order parameter. However, in the thermodynamic limit (i.e., infinite-system-size limit), we found that ft→1/2 and the activity for the inhibition dominated case (f≥ft) becomes negligible for any value of the parameters, while the first-order transition between low- and high-activity phases for f

关键词： Critical phenomena Dynamical phase transitions neuroscience, neural computation & artificial intelligence Nonequilibrium statistical mechanics Phase diagrams Phase transitions in biological systems Scaling laws of complex systems

来源：评论

学校读者我要写书评

暂无评论

Ionic plasmon-polariton in application to neurosignaling

引用

Physical Review E 2024年第3期109卷 034401-034401页

作者： J. E. Jacak W. A. Jacak Department of Quantum Technologies Wrocław University of Science and Technology Wyb. Wyspiańskiego 27 50-370 Wrocław Poland

The diffusive ion current is insufficient to explain the fast saltatory conduction observed in myelinated axons and in pain-sensing C fibers in the human nervous system, where the stimulus signal exhibits a velocity two orders of magnitude greater than the upper limit of ion diffusion velocity, even when the diffusion is accelerated by myelin, as in the discrete cable model including the Hodgkin-Huxley mechanism. The agreement with observations has been achieved in a wave-type model of stimulus signal kinetics via synchronized ion local density oscillations propagating as a wave in axons periodically corrugated by myelin segments in myelinated axons, or by periodically distributed rafts with clusters of Na+ channels in C fibers. The resulting so-called plasmon-polariton model for saltatory conduction reveals also the specific role of myelin, which is different from what was previously thought. This can be important for identifying a new target for the future treatment of demyelination diseases.

关键词： Neuronal dynamics neuroscience, neural computation & artificial intelligence Plasmonics Surface plasmon polariton Random phase approximation

来源：评论

学校读者我要写书评

暂无评论

Spiking mode-based neural networks

引用

Physical Review E 2024年第2期110卷 024306-024306页

作者： Zhanghan Lin Haiping Huang PMI Lab School of Physics Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices

Spiking neural networks play an important role in brainlike neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large-scale spiking neural network is that updating all weights is quite expensive. Furthermore, after training, all information related to the computational task is hidden into the weight matrix, prohibiting us from a transparent understanding of circuit mechanisms. Therefore, in this work, we address these challenges by proposing a spiking mode-based training protocol, where the recurrent weight matrix is explained as a Hopfield-like multiplication of three matrices: input modes, output modes, and a score matrix. The first advantage is that the weight is interpreted by input and output modes and their associated scores characterizing the importance of each decomposition term. The number of modes is thus adjustable, allowing more degrees of freedom for modeling the experimental data. This significantly reduces the training cost because of significantly reduced space complexity for learning. Training spiking networks is thus carried out in the mode-score space. The second advantage is that one can project the high-dimensional neural activity (filtered spike train) in the state space onto the mode space which is typically of a low dimension, e.g., a few modes are sufficient to capture the shape of the underlying neural manifolds. We successfully apply our framework in two computational tasks—digit classification and selective sensory integration tasks. Our method thus accelerates the training of spiking neural networks by a Hopfield-like decomposition, and moreover this training leads to low-dimensional attractor structures of high-dimensional neural dynamics.

关键词： neural encoding neuroscience, neural computation & artificial intelligence Spiking neurons artificial neural networks Chaos & nonlinear dynamics

来源：评论

学校读者我要写书评

暂无评论

Decomposing Thermodynamic Dissipation of Linear Langevin Systems via Oscillatory Modes and Its Application to neural Dynamics

引用

Physical Review X 2024年第4期14卷 041003页

作者： Daiki Sekizawa Sosuke Ito Masafumi Oizumi Department of General Systems Studies The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Universal Biology Institute The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan

Recent developments in stochastic thermodynamics have elucidated various relations between the entropy production rate (thermodynamic dissipation) and the physical limits of information processing in nonequilibrium dynamical systems. These findings have been actively utilized and have opened new perspectives in the analysis of real biological systems. In neuroscience also, the importance of quantifying entropy production has attracted increasing attention as a means to understand the properties of information processing in the brain. However, the relationship between entropy production rate and oscillations, which is prevalent in many biological systems, is unclear. For example, neural oscillations, such as delta, theta, and alpha waves, play crucial roles in brain information processing. Here, we derive a novel decomposition of the entropy production rate of linear Langevin systems. We show that one of the components of the entropy production rate, called the housekeeping entropy production rate, can be decomposed into independent positive contributions from oscillatory modes. Our decomposition enables us to calculate the contribution of oscillatory modes to the housekeeping entropy production rate. In addition, when the noise matrix of the Langevin equation is diagonal, the contribution of each oscillatory mode is further decomposed into the contribution of each element of the system. To demonstrate the utility of our decomposition, we apply our decomposition to an electrocorticography dataset recorded during awake and anesthetized conditions in monkeys, wherein the properties of oscillations change drastically. We show the consistent trends across different monkeys; i.e., the contribution of oscillatory modes from the delta band are larger in the anesthetized condition than in the awake condition, while those from the higher-frequency bands, such as the theta band, are smaller. These results allow us to interpret the change in neural oscillation in terms of stoch

关键词： neural information neuroscience, neural computation & artificial intelligence Nonequilibrium & irreversible thermodynamics Stochastic thermodynamics Brain

来源：评论

学校读者我要写书评

暂无评论

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators

引用

Physical Review E 2024年第4期110卷 044401页

作者： Sinan Aktay Leonard M. Sander Michal Zochowski Biophysics Program Department of Physics

Neuromodulatory processes in the brain can critically change signal processing on a cellular level, leading to dramatic changes in network level reorganization. Here, we use coupled nonidentical Kuramoto oscillators to investigate how changes in the shape of phase response curves from Type 1 to Type 2, mediated by varying ACh levels, coupled with activity-dependent plasticity may alter network reorganization. We first show that, when plasticity is absent, the Type 1 networks with symmetric adjacency matrix, as expected, exhibit asynchronous dynamics with oscillators of the highest natural frequency robustly evolving faster in terms of their phase dynamics. However, interestingly, Type 1 networks with an asymmetric connectivity matrix can produce stable synchrony (so-called splay states) with complex phase relationships. At the same time, Type 2 networks synchronize independent of the symmetry of their connectivity matrix, with oscillators locked so that those with higher natural frequency have a constant phase lead as compared to those with lower natural frequency. This relationship establishes a robust mapping between the frequency and oscillators' phases in the network, leading to structure and frequency mapping when plasticity is present. Finally, we show that biologically realistic, phase-locking dependent, connection plasticity naturally produces splay states in Type 1 networks that do not display the structure-frequency reorganization observed in synchronized Type II networks. These results indicate that the formation of splay states in the brain could be a common phenomenon.

关键词： Clustering Collective behavior in networks neural basis of learning & memory neuroscience, neural computation & artificial intelligence Synchronization transition

来源：评论

学校读者我要写书评

暂无评论

Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain

引用

Physical Review X 2024年第3期14卷 031050页

作者： Richard E. Rosch Dominic R. W. Burrows Christopher W. Lynn Arian Ashourvan Department of Psychology

Brain activity is characterized by brainwide spatiotemporal patterns that emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to in vivo recordings of whole-brain activity at single-neuron resolution and, therefore, allows the study of how large-scale brain dynamics emerge from local activity. In this study, we use a statistical mechanics approach—the pairwise maximum entropy model—to infer microscopic network features from collective patterns of activity in the larval zebrafish brain and relate these features to the emergence of observed whole-brain dynamics. Our findings indicate that the pairwise interactions between neural populations and their intrinsic activity states are sufficient to explain observed whole-brain dynamics. In fact, the pairwise relationships between neuronal populations estimated with the maximum entropy model strongly correspond to observed structural connectivity patterns. Model simulations also demonstrated how tuning pairwise neuronal interactions drives transitions between observed physiological regimes and pathologically hyperexcitable whole-brain regimes. Finally, we use virtual resection to identify the brain structures that are important for maintaining the brain in a physiological dynamic regime. Together, our results indicate that whole-brain activity emerges from a complex dynamical system that transitions between basins of attraction whose strength and topology depend on the connectivity between brain areas.

关键词： Biological neural networks Network phase transitions Network structure Neuronal dynamics neuroscience, neural computation & artificial intelligence Patterns in complex systems Phase transitions in biological systems

来源：评论

学校读者我要写书评

暂无评论

Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain

引用

Physical Review Letters 2024年第20期133卷 208401-208401页

作者： Rubén Calvo Carles Martorell Guillermo B. Morales Serena Di Santo Miguel A. Muñoz Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional Universidad de Granada E-18071 Granada Spain

Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly support the hypothesis that neural dynamics operate near the edge of instability. However, these and related analyses often fail to capture the intricate temporal structure of brain activity, as they primarily rely on time-integrated measurements across neurons. Here, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Furthermore, we introduce a method for projecting brain activity onto a basis of spatiotemporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in criticality signatures, effective dimensionality, and spatiotemporal activity patterns between healthy subjects and individuals with Parkinson’s disease, highlighting its potential impact.

关键词： Critical phenomena Fluctuations & noise Neuronal dynamics neuroscience, neural computation & artificial intelligence Brain

来源：评论

学校读者我要写书评

暂无评论

Mean-field method for generic conductance-based integrate-and-fire neurons with finite timescales

引用

Physical Review E 2024年第2期109卷 024406-024406页

作者： Marcelo P. Becker Marco A. P. Idiart Department of Physics Institute of Physics Federal University of Rio Grande do Sul Porto Alegre Brazil

The construction of transfer functions in theoretical neuroscience plays an important role in determining the spiking rate behavior of neurons in networks. These functions can be obtained through various fitting methods, but the biological relevance of the parameters is not always clear. However, for stationary inputs, such functions can be obtained without the adjustment of free parameters by using mean-field methods. In this work, we expand current Fokker-Planck approaches to account for the concurrent influence of colored and multiplicative noise terms on generic conductance-based integrate-and-fire neurons. We reduce the resulting stochastic system through the application of the diffusion approximation to a one-dimensional Langevin equation. An effective Fokker-Planck is then constructed using Fox Theory, which is solved numerically using a newly developed double integration procedure to obtain the transfer function and the membrane potential distribution. The solution is capable of reproducing the transfer function and the stationary voltage distribution of simulated neurons across a wide range of parameters. The method can also be easily extended to account for different sources of noise with various multiplicative terms, and it can be used in other types of problems in principle.

关键词： Biological neural networks neuroscience, neural computation & artificial intelligence Noise

来源：评论

学校读者我要写书评

暂无评论

How Deep neural Networks Learn Compositional Data: The Random Hierarchy Model

引用

Physical Review X 2024年第3期14卷 031001-031001页

作者： Francesco Cagnetta Leonardo Petrini Umberto M. Tomasini Alessandro Favero Matthieu Wyart Institute of Physics Institute of Electrical Engineering

Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representations. However, how many training examples are required to learn such representations remains unknown. To quantitatively study this question, we introduce the random hierarchy model: a family of synthetic tasks inspired by the hierarchical structure of language and images. The model is a classification task where each class corresponds to a group of high-level features, chosen among several equivalent groups associated with the same class. In turn, each feature corresponds to a group of subfeatures chosen among several equivalent groups and so on, following a hierarchy of composition rules. We find that deep networks learn the task by developing internal representations invariant to exchanging equivalent groups. Moreover, the number of data required corresponds to the point where correlations between low-level features and classes become detectable. Overall, our results indicate how deep networks overcome the curse of dimensionality by building invariant representations and provide an estimate of the number of data required to learn a hierarchical task.

关键词： neuroscience, neural computation & artificial intelligence artificial neural networks Deep learning

来源：评论

学校读者我要写书评

暂无评论

Linear and nonlinear integrate-and-fire neurons driven by synaptic shot noise with reversal potentials

引用

Physical Review E 2024年第2期109卷 024407-024407页

作者： Magnus J. E. Richardson Warwick Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom

The steady-state firing rate and firing-rate response of the leaky and exponential integrate-and-fire models receiving synaptic shot noise with excitatory and inhibitory reversal potentials is examined. For the particular case where the underlying synaptic conductances are exponentially distributed, it is shown that the master equation for a population of such model neurons can be reduced from an integrodifferential form to a more tractable set of three differential equations. The system is nevertheless more challenging analytically than for current-based synapses: where possible, analytical results are provided with an efficient numerical scheme and code provided for other quantities. The increased tractability of the framework developed supports an ongoing critical comparison between models in which synapses are treated with and without reversal potentials, such as recently in the context of networks with balanced excitatory and inhibitory conductances.

关键词： neuroscience, neural computation & artificial intelligence Integrate-and-fire model Spiking neuron models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：