scholarly journals Synchronization patterns: from network motifs to hierarchical networks

2017 ◽  
Vol 375 (2088) ◽  
pp. 20160216 ◽  
Author(s):  
Sanjukta Krishnagopal ◽  
Judith Lehnert ◽  
Winnie Poel ◽  
Anna Zakharova ◽  
Eckehard Schöll
Keyword(s):  
Network Topology ◽  
Fractal Property ◽  
Cluster Synchronization ◽  
Partial Amplitude ◽  
Network Motifs ◽  
Amplitude Death ◽  
Hierarchical Networks ◽  
Comprehensive Picture ◽  
Self Similar ◽  
The Brain

We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart–Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks. This article is part of the themed issue ‘Horizons of cybernetical physics’.

Motif Analysis and the Periodic Structural Changes in an Organizational Email-Based Social Network

2011 ◽  
pp. 292-302
Author(s):  
Krzysztof Juszczyszyn ◽  
Katarzyna Musial
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Topology ◽  
Structural Changes ◽  
Structural Parameters ◽  
Local Network ◽  
Network Motifs ◽  
Motif Analysis ◽  
University Of Technology ◽  
E Mail

Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. In this work the motif analysis of the e-mail network of the Wroclaw University of Technology, consisting of over 4000 nodes was conducted. Temporal changes in the network structure during the period of 20 months were analysed and the correlations between global structural parameters of the network and motif distribution were found. These results are to be used in the development of methods dedicated for fast estimating of the properties of complex internet-based social networks.

scholarly journals Geometric renormalization unravels self-similarity of the multiscale human connectome

2020 ◽  
Vol 117 (33) ◽  
pp. 20244-20253 ◽  
Author(s):  
Muhua Zheng ◽  
Antoine Allard ◽  
Patric Hagmann ◽  
Yasser Alemán-Gómez ◽  
M. Ángeles Serrano
Keyword(s):  
Healthy Subjects ◽  
Multiple Scales ◽  
Structural Connectivity ◽  
Coarse Graining ◽  
Brain Regions ◽  
Self Similarity ◽  
Digital Reconstruction ◽  
Self Similar ◽  
The Brain ◽  
Organizing Principles

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.

scholarly journals A Framework for Brain Atlases: Lessons from seizure Dynamics

2021 ◽  
Author(s):  
Andrew Y. Revell ◽  
Alexander B. Silva ◽  
T. Campbell Arnold ◽  
Joel M. Stein ◽  
Sandhitsu R. Das ◽  
...  
Keyword(s):  
Network Topology ◽  
General Framework ◽  
Neurological Diseases ◽  
Experimental System ◽  
Functional Changes ◽  
Disease Biology ◽  
Neuroscience Research ◽  
Function Correlation ◽  
The Relationship ◽  
The Brain

Understanding the relationship between the brain's structural anatomy and neural activity is essential in identifying the structural therapeutic targets linked to the functional changes seen in neurological diseases. An implicit challenge is that the varying maps of the brain, or atlases, used across the neuroscience literature to describe the different regions of the brain alters the hypotheses and predictions we make about the brain's function of those regions. Here we demonstrate how parcellation scale, shape, and anatomical coverage of these atlases impact network topology, structure-function correlation (SFC), and the hypotheses we make about epilepsy disease biology. Through the lens of our disease system, we propose a general framework to evaluate the validity of an atlas used in an experimental system. This framework aims to maximize the descriptive, explanatory, and predictive validity of these atlases. Broadly, our framework strives to augment neuroscience research utilizing the various atlases published over the last

Experimental investigation of amplitude death in delay-coupled double-scroll circuits with randomly time-varying network topology

2020 ◽  
Vol 99 (4) ◽  
pp. 3155-3168 ◽  
Author(s):  
Shinnosuke Masamura ◽  
Tetsu Iwamoto ◽  
Yoshiki Sugitani ◽  
Keiji Konishi ◽  
Naoyuki Hara
Keyword(s):  
Experimental Investigation ◽  
Network Topology ◽  
Time Varying ◽  
Amplitude Death

scholarly journals Emergence of Network Motifs in Deep Neural Networks

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 204
Author(s):  
Matteo Zambra ◽  
Amos Maritan ◽  
Alberto Testolin
Keyword(s):  
Neural Networks ◽  
Network Topology ◽  
Deep Neural Networks ◽  
Network Science ◽  
Network Motifs ◽  
Structural And Functional Properties ◽  
Topological Modules ◽  
Weight Initialization ◽  
Learning Principles ◽  
Fully Connected

Network science can offer fundamental insights into the structural and functional properties of complex systems. For example, it is widely known that neuronal circuits tend to organize into basic functional topological modules, called network motifs. In this article, we show that network science tools can be successfully applied also to the study of artificial neural networks operating according to self-organizing (learning) principles. In particular, we study the emergence of network motifs in multi-layer perceptrons, whose initial connectivity is defined as a stack of fully-connected, bipartite graphs. Simulations show that the final network topology is shaped by learning dynamics, but can be strongly biased by choosing appropriate weight initialization schemes. Overall, our results suggest that non-trivial initialization strategies can make learning more effective by promoting the development of useful network motifs, which are often surprisingly consistent with those observed in general transduction networks.

scholarly journals ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE

2007 ◽  
Vol 10 (02) ◽  
pp. 155-172 ◽  
Author(s):  
ANDRÉ LEIER ◽  
P. DWIGHT KUO ◽  
WOLFGANG BANZHAF
Keyword(s):  
Gene Regulatory Networks ◽  
Network Topology ◽  
Regulatory Networks ◽  
Genome Duplication ◽  
Network Motif ◽  
Network Motifs ◽  
Gene Regulatory ◽  
Artificial Gene ◽  
Artificial Model ◽  
Regulatory Sites

Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.

FRACTAL DIMENSION AND LACUNARITY OF TRACTOGRAPHY IMAGES OF THE HUMAN BRAIN

Fractals ◽  
2009 ◽  
Vol 17 (02) ◽  
pp. 181-189 ◽  
Author(s):  
P. KATSALOULIS ◽  
D. A. VERGANELAKIS ◽  
A. PROVATA
Keyword(s):  
Fractal Dimension ◽  
Human Brain ◽  
Fractal Dimensions ◽  
Resonance Imaging ◽  
Self Similarity ◽  
Healthy Human ◽  
Box Counting ◽  
Self Similar ◽  
Box Method ◽  
The Brain

Tractography images produced by Magnetic Resonance Imaging scans have been used to calculate the topology of the neuron tracts in the human brain. This technique gives neuroanatomical details, limited by the system resolution properties. In the observed scales the images demonstrated the statistical self-similar structure of the neuron axons and its fractal dimensions were estimated using the classic Box Counting technique. To assess the degree of clustering in the neural tracts network, lacunarity was calculated using the Gliding Box method. The two-dimensional tractography images were taken from four subjects using various angles and different parts in the brain. The results demonstrated that the average estimated fractal dimension of tractography images is approximately Df = 1.60 with standard deviation 0.11 for healthy human-brain tissues, and it presents statistical self-similarity features similar to many other biological root-like structures.

scholarly journals Multiscale criticality measures as general-purpose gauges of proper brain function

2019 ◽  
Author(s):  
Tomer Fekete ◽  
Hermann Hinrichs ◽  
Jacobo Diego Sitt ◽  
Hans-Jochen Heinze ◽  
Oren Shriki
Keyword(s):  
Information Processing ◽  
General Purpose ◽  
Processing Capacity ◽  
Brain Health ◽  
Hierarchical Networks ◽  
Information Processing Capacity ◽  
Eeg Data ◽  
Hierarchical Complexity ◽  
Processing Information ◽  
The Brain

ABSTRACTThe brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). We applied MsCr to MEG and EEG data in four telling degraded information processing scenarios: disorders of consciousness, mild cognitive impairment, schizophrenia and preictal activity. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

Sign in / Sign up

Export Citation Format

Share Document