Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus.
Ontology highlight
ABSTRACT:
SUBMITTER: Larremore DB
PROVIDER: S-EPMC3183795 | biostudies-other | 2011 Jun
REPOSITORIES: biostudies-other
Similar Datasets
Project description:We present a discrete model of stochastic excitability by a low-dimensional set of delayed integral equations governing the probability in the rest state, the excited state, and the refractory state. The process is a random walk with discrete states and nonexponential waiting time distributions, which lead to the incorporation of memory kernels in the integral equations. We extend the equations of a single unit to the system of equations for an ensemble of globally coupled oscillators, derive the mean field equations, and investigate bifurcations of steady states. Conditions of destabilization are found, which imply oscillations of the mean fields in the stochastic ensemble. The relation between the mean field equations and the paradigmatic Kuramoto model is shown.
| S-EPMC2652551 | biostudies-other
Project description:While much recent research has focused on understanding isolated cascades of networks, less attention has been given to dynamical processes on networks exhibiting repeated cascades of opposing influence. An example of this is the dynamic behaviour of financial markets where cascades of buying and selling can occur, even over short timescales. To model these phenomena, a stochastic pulse-coupled oscillator network with upper and lower thresholds is described and analysed. Numerical confirmation of asynchronous and synchronous regimes of the system is presented, along with analytical identification of the fixed point state vector of the asynchronous mean field system. A lower bound for the finite system mean field critical value of network coupling probability is found that separates the asynchronous and synchronous regimes. For the low-dimensional mean field system, a closed-form equation is found for cascade size, in terms of the network coupling probability. Finally, a description of how this model can be applied to interacting agents in a financial market is provided.
| S-EPMC4161966 | biostudies-other
Project description:The speed and paths of synchronization play a key role in the function of a system, which has not received enough attention up to now. In this work, we study the synchronization process of coupled logistic maps that reveals the common features of low-dimensional dissipative systems. A slowing down of synchronization process is observed, which is a novel phenomenon. The result shows that there are two typical kinds of transient process before the system reaches complete synchronization, which is demonstrated by both the coupled multiple-period maps and the coupled multiple-band chaotic maps. When the coupling is weak, the evolution of the system is governed mainly by the local dynamic, i.e., the node states are attracted by the stable orbits or chaotic attractors of the single map and evolve toward the synchronized orbit in a less coherent way. When the coupling is strong, the node states evolve in a high coherent way toward the stable orbit on the synchronized manifold, where the collective dynamics dominates the evolution. In a mediate coupling strength, the interplay between the two paths is responsible for the slowing down. The existence of different synchronization paths is also proven by the finite-time Lyapunov exponent and its distribution.
| S-EPMC4810433 | biostudies-other
Project description:The goal of this study was to investigate whether histone modifcations and gene expression changes in mouse macrophages induced by beta glucan could persist over the course of many cell divisions following stimulus removal
2023-02-22 | GSE225855 | GEO
Project description:Kirigami, the creative art of paper cutting, is a promising paradigm for mechanical metamaterials. However, to make kirigami-inspired structures a reality requires controlling the topology of kirigami to achieve connectivity and rigidity. We address this question by deriving the maximum number of cuts (minimum number of links) that still allow us to preserve global rigidity and connectivity of the kirigami. A deterministic hierarchical construction method yields an efficient topological way to control both the number of connected pieces and the total degrees of freedom. A statistical approach to the control of rigidity and connectivity in kirigami with random cuts complements the deterministic pathway, and shows that both the number of connected pieces and the degrees of freedom show percolation transitions as a function of the density of cuts (links). Together, this provides a general framework for the control of rigidity and connectivity in planar kirigami.
| S-EPMC7060672 | biostudies-literature
Project description:Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity.
| S-EPMC3990528 | biostudies-literature
Project description:Familiarity of odor-odor combinations is enhanced through food intake in daily life. As familiarity increases, the perceptual boundary between two odors may become ambiguous; therefore, we hypothesized that exposure to one odor would delay detection of the other in a high-familiarity combination but not in a low-familiarity combination. To test this hypothesis, we measured the speed of odor detection using two types of background stimuli (black tea odor and odorless air) and two types of target stimuli (lemon odor and almond odor). For Japanese participants, the combination of black tea and lemon odor has high familiarity, whereas the combination of black tea and almond odors has low familiarity. Reaction time for detection of target stimulus was measured by inserting a pulsed target stimulus into the flow of the background stimulus (i.e., replacing the background stimulus with the target stimulus for a short time). Reaction time for detection of lemon odor was significantly longer under the black tea odor condition than under the odorless air condition. Reaction time for detection of almond odor was similar between the black tea odor and odorless air conditions. These results are in line with the hypothesis that familiarity of an odor-odor combination affects odor detection speed. Further investigations are required to reach more robust conclusions.
| S-EPMC8184818 | biostudies-literature
Project description:Schizophrenia is a severe and heritable brain disorder. Language impairment has been hypothesized to spur its onset and underlie the characteristic symptoms. In this study, we investigate whether altered topological pattern of the language processing brain network exists and could be a potential biomarker of schizophrenia. We hypothesized that both patients with schizophrenia and the genetic high risk population would show significantly weakened efficiencies of the network hubs for normal language processing, especially at left inferior frontal and bilateral temporal lobes.Language task-based fMRI data from 21 patients with schizophrenia, 22 genetic high risk subjects and 36 controls were analyzed. Graph theoretic and post hoc analyses of the fMRI data, and correlations between the functional network features and scores of language tests were carried out.Compared to controls, patients with schizophrenia and the high risk subjects showed significantly weakened network hubs in left inferior frontal and right fusiform gyri. A unique topology of super active and intercommunicating network hubs at left fusiform gyrus and right inferior/middle frontal gyri, which were associated with the behavioral language impairment was found in the patient group, compared to the high risk and control groups.Aberrant systems-level topology of language processing network, especially significantly weakened network hubs in left inferior frontal and right fusiform gyri, may serve as a candidate biomarker of schizophrenia. Supported by existing findings, the hyperactive left fusiform gyrus communicating with right frontal lobe might be the key neurophysiological component causing hallucinations in schizophrenia. These findings provided a new systems-level diagnostic target for the disorder.
| S-EPMC3463735 | biostudies-literature
Project description:MOTIVATION:Conventional phylogenetic analysis for characterizing the relatedness between taxa typically assumes that a single relationship exists between species at every site along the genome. This assumption fails to take into account recombination which is a fundamental process for generating diversity and can lead to spurious results. Recombination induces a localized phylogenetic structure which may vary along the genome. Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny along multiple sequence alignments while accounting for rate heterogeneity; the hidden states refer to the unobserved phylogenic topology underlying the relatedness at a genomic location. The dimensionality of the number of hidden states (topologies) and their structure are random (not known a priori) and are sampled using Markov chain Monte Carlo algorithms. The HMM structure allows us to analytically integrate out over all possible changepoints in topologies as well as all the unknown branch lengths. RESULTS:We demonstrate our approach on simulated data and also to the genome of a suspected HIV recombinant strain as well as to an investigation of recombination in the sequences of 15 laboratory mouse strains sequenced by Perlegen Sciences. Our findings indicate that our method allows us to distinguish between rate heterogeneity and variation in phylogeny caused by recombination without being restricted to 4-taxa data.
| S-EPMC2639012 | biostudies-literature
Project description:MotivationThe time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here.ResultsWe develop a novel filtering approach for the LNA in stochastic systems with distributed delays, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1, a key gene involved in the mammalian central circadian clock, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus.Availability and implementationProgrammes are written in MATLAB and Statistics Toolbox Release 2016 b, The MathWorks, Inc., Natick, Massachusetts, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6477979 | biostudies-literature