Frontiers in network physiology最新文献

Sudden cardiac death (SCD) is often precipitated by reentrant arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF), whose underlying dynamics are frequently sustained by spiral waves of electrical activity. Disrupting these waves can restore normal rhythm, but conventional low-energy pacing strategies are often ineffective in VF, where high-frequency, multi-wave interactions dominate. Resonant feedback-controlled antitachycardia pacing (rF-ATP), which times global electrical stimuli based on real-time feedback from the tissue, has been shown to robustly terminate single spirals under diverse conditions. However, its impact on interacting spiral waves-arguably a more realistic substrate for life-threatening arrhythmias-remains unexplored. Here, we use numerical simulations to investigate the effect of rF-ATP on figure-of-eight reentry, a clinically relevant configuration consisting of two counter-rotating spirals. We show that rF-ATP consistently terminates this pattern, regardless of feedback point location, through two distinct dynamical pathways: mutual collision of phase singularities or annihilation at inexcitable boundaries. We further demonstrate the method's efficacy across variations in feedback point and spiral arrangement, indicating robustness to geometrical and positional heterogeneity. These results highlight rF-ATP as a promising low-energy intervention for complex reentrant structures and provide mechanistic insight into feedback-driven control of multi-core spiral wave dynamics in cardiac tissue.

Cardiac dynamics is governed by complex electrical wave patterns, with disruptions leading to pathological conditions like atrial or ventricular fibrillation. Experimentally electrical excitation waves can be made visible by optical mapping using fluorescent dyes. While this imaging technique has enabled detailed studies of cardiac wave dynamics, the manual analysis of activation and phase maps often limits the ability to systematically identify and quantify wave patterns. This study employs a wave tracking algorithm that constructs a graph-based representation of wave dynamics. With that the algorithm detects key events such as wave emergence, splitting, and merging. Applied to both simulated cardiac tissue and experimental data from cell cultures, the algorithm identifies and quantifies wave patterns as wave event networks. Initial results demonstrate its utility in filtering for and focusing on dominant dynamics, providing a robust tool for analyzing cardiac wave patterns. This approach offers potential applications, e.g., to study the effects of external stimuli on cardiac excitation patterns and to better understand the mechanisms involved.

It has been shown before that two species of diffusing particles can separate from each other by the mechanism of reciprocally concentration-dependent diffusivity: the presence of one species amplifies the diffusion coefficient of the respective other one, causing the two densities of particles to separate spontaneously. In a minimal model, this could be observed with a quadratic dependence of the diffusion coefficient on the density of the other species. Here, we consider a more realistic sigmoidal dependence as a logistic function on the other particle's density averaged over a finite sensing radius. The sigmoidal dependence accounts for the saturation effects of the diffusion coefficients, which cannot grow without bounds. We show that sigmoidal (logistic) cross-diffusion leads to a new regime in which a homogeneous disordered (well-mixed) state and a spontaneously separated ordered (demixed) state coexist, forming two long-lived metastable configurations. In systems with a finite number of particles, random fluctuations induce repeated transitions between these two states. By tracking an order parameter that distinguishes mixed from demixed phases, we measure the corresponding mean residence in each state and demonstrate that one lifetime increases and the other decreases as the logistic coupling parameter is varied. The system thus displays typical features of a first-order phase transition, including hysteresis for large particle numbers. In addition, we compute the correlation time of the order parameter and show that it exhibits a pronounced maximum within the bistable parameter range, growing exponentially with the total particle number.

In this perspective, we introduce the Precision Principle as a unifying theoretical framework to explain self-organization across biological systems. Drawing from neurobiology, systems theory, and computational modeling, we propose that precision, understood as constraint-driven coherence, is the key force shaping the architecture, function, and evolution of nervous systems. We identify three interrelated domains: Structural Precision (efficient, modular wiring), Functional Precision (adaptive, context-sensitive circuit deployment), and Evolutionary Precision (selection-guided architectural refinement). Each domain is grounded in local operations such as spatial and temporal averaging, multiplicative co-activation, and threshold gating, which enable biological systems to achieve robust organization without centralized control. Within this framework, we introduce the Precision Coefficient, $P\left(z\right)=C\left(z\right)-\alpha R\left(z\right)$ , which formalizes the balance between network coherence and resource cost and serves as a simple quantitative outline of the principle. Conceptually, this formalism aligns with established learning mechanisms: Hebbian reinforcement provides the local substrate for weight changes, while winner-take-all and k-winners competition selectively eliminates weaker synapses, together increasing $C\left(z\right)$ and reducing redundancy within $R\left(z\right)$ . Rather than framing the theory in opposition to existing models, we aim to establish the Precision Principle as an original, integrative lens for understanding how systems sustain efficiency, flexibility, and resilience. We hope the framework inspires new research into neural plasticity, development, and artificial systems, by centering internal coherence, not prediction or control, as the primary driver of self-organizing intelligence.

Aging is associated with a decline in inter-muscular coordination and overall functional capacity. While the benefits of exercise on individual physiological systems are well established, it remains unclear whether regular training can also enhance inter-muscular network interactions and counteract age-related decline. Using a Network Physiology approach, this Case Report investigates the effects of a home-based exercise program on inter-muscular coordination in two older adults. Two older adults (aged 69 and 73) completed a 12-week program that included twice-weekly virtual group sessions, and one weekly session of moderate-intensity aerobic exercise (30 min). Before and after the intervention, participants underwent a maximal cardiopulmonary exercise test (CPET) on a motorized treadmill. During the CPET, surface electromyography (EMG) was recorded from the left and right rectus femoris and biceps femoris. Inter-muscular coordination was quantified using the Amplitude-Amplitude Cross-Frequency Coupling (ACFC) method. Ten time series of EMG band power were extracted for each muscle, representing distinct neuromuscular processes. Pearson's cross-correlation was then computed for each pair of EMG band power time series across all muscles. Pre-Intervention, both participants showed low overall link strength across all sub-networks. Post-Intervention, there was a pronounced (∼400%) increase in average link strength across all sub-networks in both participants, primarily reflecting enhanced synchronization between distinct frequency bands across the rectus femoris and biceps femoris. These preliminary findings suggest that structured exercise may enhance inter-muscular network coordination in older adults. ACFC-derived network measures offer a promising tool for detecting early age-related decline and evaluating neuromuscular adaptations to exercise interventions.

Representing and integrating continuous variables is a fundamental capability of the brain, often relying on ring attractor circuits that maintain a persistent bump of activity. To investigate how such structures can self-organize, we trained a recurrent neural network (RNN) on a ring-based path integration task using population-coded velocity inputs. The network autonomously developed a modular architecture: one subpopulation formed a stable ring attractor to maintain the integrated position, while a second, distinct subpopulation organized into a dissipative control unit that translates velocity into directional signals. Furthermore, systematic perturbations revealed that the precise topological alignment between these modules is essential for reliable integration. Our findings illustrate how functional specialization and biologically plausible representations can emerge from a general learning objective, offering insights into neural self-organization and providing a framework for designing more interpretable and robust neuromorphic systems for navigation and control.

Introduction: Real-world networks possess complex, higher-order structures that are not captured by traditional pairwise analysis methods. Q-analysis provides a powerful mathematical framework based on simplicial complexes to uncover and quantify these multi-node interactions. However, its adoption has been limited by a lack of accessible software tools.

Methods: We introduce a comprehensive Python package that implements the core methodology of Q-analysis. The package enables the construction of simplicial complexes from graphs or simplex lists and computes a suite of descriptive metrics, including structure vectors (FSV, SSV, TSV) and topological entropy. It features high-performance routines, integration with scikit-learn for machine learning workflows, and tools for statistical inference, such as permutation tests.

Results: We demonstrate the package's capabilities through a simulation study, revealing distinct higher-order topological signatures in scale-free versus configurational networks despite identical degree distributions. Application to the DBLP co-authorship dataset uncovered the evolution of collaborative structures over three decades, showing increased collaboration scale and shifts in higher-order connectivity patterns. Finally, in a network physiology application, the package identified significant disruptions in the higher-order organization of fMRI-derived brain networks in Major Depressive Disorder (MDD), characterized by a loss of high-dimensional functional components and increased fragmentation.

Discussion: The developed package makes Q-analysis accessible to a broad research audience, facilitating the exploration of higher-order interactions in complex systems. The presented applications validate its utility across diverse domains, from social networks to neuroscience. By providing an open-source tool, this work bridges a gap in network science, enabling quantitative analysis of the intricate, multi-node structures that define real-world networks.

Introduction: In this work, we introduce a novel approach to one of the historically fundamental questions in neural networks: how to encode information? More particularly, we look at temporal coding in spiking networks, where the timing of a spike as opposed to the frequency, determines the information content. In contrast to previous temporal-coding schemes, which rely on the statistical properties of populations of neurons and connections, we employ a novel synaptic plasticity mechanism that allows the timing to be learnt at the single-synapse level.

Methods: Using a formal basis from information theory, we show how a phase-coded spike train (relative to some 'reference' phase) can, in fact, multiplex multiple different information signals onto the same spike train, significantly improving overall information capacity. We furthermore derive limits on the channel capacity in the phase-coded spiking case, and show that the learning rule also has a continuous derivative in the input-output relation, making it potentially amenable to classical learning rules from artificial neural networks such as backpropagation.

Results: Using a simple demonstration network, we show the multiplexing of different signals onto the same connection, and demonstrate that different synapses indeed can adapt using this learning rule, to specialise to different interspike intervals (i.e., phase relationships). The overall approach allows for denser encoding, and thus energy efficiency, in neural networks for complex tasks, allowing smaller and more compact networks to achieve combinations of tasks which traditionally would have required high-dimensional embeddings.

Discussion: Although carried out as a study in computational spiking neural networks, the results may have insights for functional neuroscience, and suggest links to mechanisms that have been shown from neuroscientific studies to support temporal coding. To the best of our knowledge, this is the first study to solve one of the outstanding problems in spiking neural networks: to demonstrate that distinct temporal codings can be distinguished through synaptic learning.

In this paper, we calculate magnitude-constrained optimal stimuli for desynchronizing a population of neurons by maximizing the Lyapunov exponent for the phase difference between pairs of neurons while simultaneously minimizing the energy which is used. This theoretical result informs the way optimal inputs can be designed for deep brain stimulation in cases where there is a biological or electronic constraint on the amount of current that can be applied. By exploring a range of parameter values, we characterize how the constraint magnitude affects the Lyapunov exponent and energy usage. Finally, we demonstrate the efficacy of this approach by considering a computational model for a population of neurons with repeated event-triggered optimal inputs.

This paper refers to an issue Haken and myself were discussing, started to work on, prepared a preliminary draft, but never managed to complete and transform it into a full-scale study and publication. Here, in memoriam of Hermann Haken, my dear friend and colleague for many years, I present it as it is - an unfinished study with some innovative ideas that will have to be further elaborated in the future.