An Artificial Neural Network for Image Classification Inspired by the Aversive Olfactory Learning Neural Circuit in Caenorhabditis elegans.

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning neural circuit in Caenorhabditis elegans (C. elegans). Although artificial neural networks (ANNs) have demonstrated remarkable performance in various tasks, they still encounter challenges including excessive parameterization, high training costs and limited generalization capabilities, etc.

Dynamic multilayer networks reveal mind wandering.

Introduction: Mind-wandering is a highly dynamic phenomenon involving frequent fluctuations in cognition. However, the dynamics of functional connectivity between brain regions during mind-wandering have not been extensively studied.

Methods: We employed an analytical approach aimed at extracting recurring network states of multilayer networks built using amplitude envelope correlation and imaginary phase-locking value of delta, theta, alpha, beta, or gamma frequency band.

The "weaken-fill-repair" model for cell budding: Linking cell wall biosynthesis with mechanics.

The interplay between cellular mechanics and biochemical processes in the cell cycle is not well understood. We propose a quantitative model of cell budding in as a "weaken-fill-repair" process, linking Newtonian mechanics of the cell wall with biochemical changes that affect its properties. Our model reveals that (1) oscillations in mother cell size during budding are an inevitable outcome of the process; (2) asymmetric division is necessary for the daughter cell to maintain mechanical stiffness; and (3) although various aspects of the cell are constrained and interconnected, the budding process is governed by a single reduced parameter, ψ, which balances osmolyte accumulation with enzymatic wall-weakening to ensure homeostasis.

Motifs-based link prediction for heterogeneous multilayer networks.

Link prediction has a wide range of applications in the study of complex networks, and the current research on link prediction based on single-layer networks has achieved fruitful results, while link prediction methods for multilayer networks have to be further developed. Existing research on link prediction for multilayer networks mainly focuses on multiplexed networks with homogeneous nodes and heterogeneous edges, while there are relatively few studies on general multilayer networks with heterogeneous nodes and edges. In this context, this paper proposes a method for heterogeneous multilayer networks based on motifs for link prediction.

Learning noise-induced transitions by multi-scaling reservoir computing.

Noise is usually regarded as adversarial to extracting effective dynamics from time series, such that conventional approaches usually aim at learning dynamics by mitigating the noisy effect. However, noise can have a functional role in driving transitions between stable states underlying many stochastic dynamics. We find that leveraging a machine learning model, reservoir computing, can learn noise-induced transitions.

Nucleation phenomena and extreme vulnerability of spatial k-core systems.

K-core percolation is a fundamental dynamical process in complex networks with applications that span numerous real-world systems. Earlier studies focus primarily on random networks without spatial constraints and reveal intriguing mixed-order transitions. However, real-world systems, ranging from transportation and communication networks to complex brain networks, are not random but are spatially embedded.

Motif-based community detection in heterogeneous multilayer networks.

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics.

Temporal segmentation of EEG based on functional connectivity network structure.

In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure.

Rigorous Criteria for the Collapse of Nonlinear Cooperative Networks.

Many real-world complex systems, when hitting a tipping point, undergo irreversible sudden shifts that can eventually take a great toll on humanity and the natural world, such as ecosystem collapses, disease outbreaks, etc. Previous work has adopted approximations to predict the tipping points, but due to the nature of nonlinearity, this may lead to unexpected errors in predicting real-world systems. Here we obtain the rigorous bounds of the tipping points for general nonlinear cooperative networks.

Long-range connections are crucial for synchronization transition in a computational model of Drosophila brain dynamics.

The synchronization transition type has been the focus of attention in recent years because it is associated with many functional characteristics of the brain. In this paper, the synchronization transition in neural networks with sleep-related biological drives in Drosophila is investigated. An electrical synaptic neural network is established to research the difference between the synchronization transition of the network during sleep and wake, in which neurons regularly spike during sleep and chaotically spike during wake.

Target recovery of the economic system based on the target reinforcement path method.

An effective and stable operation of an economic system leads to a prosperous society and sustainable world development. Unfortunately, the system faces inevitable perturbations of extreme events and is frequently damaged. To maintain the system's stability, recovering its damaged functionality is essential and is complementary to strengthening its resilience and forecasting extreme events.

Modeling multi-opinion propagation in complex systems with heterogeneous relationships via Potts model on signed networks.

This paper investigates how the heterogenous relationships around us affect the spread of diverse opinions in the population. We apply the Potts model, derived from condensed matter physics on signed networks, to multi-opinion propagation in complex systems with logically contradictory interactions. Signed networks have received increasing attention due to their ability to portray both positive and negative associations simultaneously, while the Potts model depicts the coevolution of multiple states affected by interactions.

Ladderpath Approach: How Tinkering and Reuse Increase Complexity and Information.

The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory and exobiology. Many measures of these quantities are either difficult to compute, rely on the statistical notion of information, or can only be applied to strings. Based on assembly theory, we propose the notion of a , which describes how an object can be decomposed into hierarchical structures using repetitive elements.

Impactful scientists have higher tendency to involve collaborators in new topics.

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate.

Classifying olfactory projection neuron boutons by quantitative analysis of electron microscopic reconstruction.

In , olfactory projection neurons (PNs) convey odor information from the antenna lobe to higher brain regions. Recent transcriptomic studies reveal a large diversity of transcription factors, cell-surface molecules, neurotransmitter-coding, and neuropeptide-coding genes in PNs; however, their structural diversity remains unknown. Herein, we achieved a volumetric reconstruction of 89 PN boutons under Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and quantitatively analyzed the internal presynaptic active zones (PAZs) and dense-core vesicles (DCVs).

Dynamical analysis of evolutionary public goods game on signed networks.

In evolutionary dynamics, the population structure and multiplayer interactions significantly impact the evolution of cooperation levels. Previous works mainly focus on the theoretical analysis of multiplayer games on regular networks or pairwise games on complex networks. Combining these two factors, complex networks and multiplayer games, we obtain the fixation probability and fixation time of the evolutionary public goods game in a structured population represented by a signed network.

Guiding propagation to localized target nodes in complex networks.

Spreading is an important type of dynamics in complex networks that can be used to model numerous real processes such as epidemic contagion and information propagation. In the literature, there are many methods in vital node identification and node immunization proposed for controlling the spreading processes. As a novel research problem, target spreading aims to minimize or maximize propagation toward a group of target nodes.

Collective Dynamics of Neural Networks With Sleep-Related Biological Drives in Drosophila.

The collective electrophysiological dynamics of the brain as a result of sleep-related biological drives in Drosophila are investigated in this paper. Based on the Huber-Braun thermoreceptor model, the conductance-based neurons model is extended to a coupled neural network to analyze the local field potential (LFP). The LFP is calculated by using two different metrics: the mean value and the distance-dependent LFP.

Fresh teams are associated with original and multidisciplinary research.

Teamwork is one of the most prominent features in modern science. It is now well understood that team size is an important factor that affects the creativity of the team. However, the crucial question of how the character of research studies is related to the freshness of a team remains unclear.

Characterizing dissimilarity of weighted networks.

Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network.

Stability of spatially discordant repolarization alternans in cardiac tissue.

Cardiac alternans, a period-2 behavior of excitation and contraction of the heart, is a precursor of ventricular arrhythmias and sudden cardiac death. One form of alternans is repolarization or action potential duration alternans. In cardiac tissue, repolarization alternans can be spatially in-phase, called spatially concordant alternans, or spatially out-of-phase, called spatially discordant alternans (SDA).

Magnitude and significance of the peak of early embryonic mortality.

Embryonic development is of great importance because it determines congenital anomalies and influences their severity. However, little is known about the actual probabilities of success or failure and about the nature of early embryonic defects. Here, we propose that the analysis of embryonic mortality as a function of post-fertilization time provides a simple way to identify major defects.