scholarly journals Adaptive Rewiring in Non-Uniform Coupled Oscillators

2021 ◽  
pp. 1-40
Author(s):  
MohammadHossein Manuel Haqiqatkhah ◽  
Cees van Leeuwen
Keyword(s):  
Information Processing ◽  
Small World ◽  
Network Evolution ◽  
Memory Storage ◽  
Network Activity ◽  
Stimulus Selection ◽  
Connection Strength ◽  
Rich Club ◽  
Coupling Strengths ◽  
Random Coupling

Abstract Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, i.e., networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether non-uniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

scholarly journals Adaptive Rewiring in Non-Uniform Coupled Oscillators

2020 ◽  
Author(s):  
MohammadHossein Manuel Haqiqatkhah ◽  
Cees van Leeuwen
Keyword(s):  
Information Processing ◽  
Small World ◽  
Network Evolution ◽  
Memory Storage ◽  
Network Activity ◽  
Stimulus Selection ◽  
Connection Strength ◽  
Rich Club ◽  
Coupling Strengths ◽  
Random Coupling

Structural plasticity of the brain can be represented in highly simplified form as adaptive rewiring; the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to complex networks, i.e. networks with modular small-world structures and a rich club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether non-uniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators were kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with development of network complexity. Whereas these subsets form distinctive structural and functional communities, this does not interfere with emergence of the characteristic network complexity. These results suggest that adaptive rewiring is robust against, and can operate in, information processing networks.

Timing of Network Synchronization By Refractory Mechanisms

2003 ◽  
Vol 90 (6) ◽  
pp. 3902-3911 ◽  
Author(s):  
Urs Achim Wiedemann ◽  
Anita Lüthi
Keyword(s):  
Neural Development ◽  
Background Activity ◽  
Synaptic Depression ◽  
Network Activity ◽  
Intrinsic Activity ◽  
Cellular Mechanisms ◽  
Avalanche Effect ◽  
Stochastic Fluctuations ◽  
Cellular Factors ◽  
Random Coupling

Even without active pacemaker mechanisms, temporally patterned synchronization of neural network activity can emerge spontaneously and is involved in neural development and information processing. Generation of spontaneous synchronization is thought to arise as an alternating sequence between a state of elevated excitation followed by a period of quiescence associated with neuronal and/or synaptic refractoriness. However, the cellular factors controlling recruitment and timing of synchronized events have remained difficult to specify, although the specific temporal pattern of spontaneous rhythmogenesis determines its impact on developmental processes. We studied spontaneous synchronization in a model of 600–1,000 integrate-and-fire neurons interconnected with a probability of 5–30%. One-third of neurons generated spontaneous discharges and provided a background of intrinsic activity to the network. The heterogeneity and random coupling of these neurons maintained this background activity asynchronous. Refractoriness was modeled either by use-dependent synaptic depression or by cellular afterhyperpolarization. In both cases, the recruitment of neurons into spontaneous synchronized discharges was determined by the interplay of refractory mechanisms with stochastic fluctuations in background activity. Subgroups of easily recruitable neurons served as amplifiers of these fluctuations, thereby initiating a cascade-like recruitment of neurons (“avalanche effect”). In contrast, timing depended on the precise implementation of neuronal refractoriness and synaptic connectivity. With synaptic depression, neuronal synchronization always occurred stochastically, whereas with cellular afterhyperpolarization, stochastic turned into periodic behavior with increasing synaptic strength. These results associate the type of refractory mechanism with the temporal statistics and the mechanism of synchronization, thereby providing a framework for differentiating between cellular mechanisms of spontaneous rhythmogenesis.

Cascading failures of interdependent modular small-world networks

2016 ◽  
Vol 30 (18) ◽  
pp. 1650174 ◽  
Author(s):  
Guowei Zhu ◽  
Xianpei Wang ◽  
Meng Tian ◽  
Dangdang Dai ◽  
Jiachuan Long ◽  
...  
Keyword(s):  
Broad Class ◽  
Small World ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Small World Networks ◽  
Connected Component ◽  
Random Coupling ◽  
Global And Local ◽  
The Relationship ◽  
Local Perspectives

Much empirical evidence shows that many real-world networks fall into the broad class of small-world networks and have a modular structure. The modularity has been revealed to have an important effect on cascading failure in isolated networks. However, the corresponding results for interdependent modular small-world networks remain missing. In this paper, we investigate the relationship between cascading failures and the intra-modular rewiring probabilities and inter-modular connections under different coupling preferences, i.e. random coupling with modules (RCWM), assortative coupling in modules (ACIM) and assortative coupling with modules (ACWM). The size of the largest connected component is used to evaluate the robustness from global and local perspectives. Numerical results indicate that increasing intra-modular rewiring probabilities and inter-modular connections can improve the robustness of interdependent modular small-world networks under intra-attacks and inter-attacks. Meanwhile, experiments on three coupling strategies demonstrate that ACIM has a better effect on preventing the cascading failures compared with RCWM and ACWM. These results can be helpful to allocate and optimize the topological structure of interdependent modular small-world networks to improve the robustness of such networks.

Epidemic spreading on evolving networks

2019 ◽  
Vol 33 (23) ◽  
pp. 1950266 ◽  
Author(s):  
Jin-Xuan Yang
Keyword(s):  
Preferential Attachment ◽  
Small World ◽  
Network Evolution ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Scale Free ◽  
Evolving Networks ◽  
Power Law Exponent ◽  
Free Network ◽  
Over Time

Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

scholarly journals Sampled-Data Synchronization for Complex Dynamical Networks with Time-Varying Coupling Delay and Random Coupling Strengths

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Jian-An Wang ◽  
Xin-Yu Wen
Keyword(s):  
Sampling Period ◽  
Complex Dynamical Networks ◽  
Data Synchronization ◽  
Linear Matrix ◽  
Time Varying ◽  
Sampled Data ◽  
Dynamical Networks ◽  
Coupling Delay ◽  
Coupling Strengths ◽  
Random Coupling

This paper is concerned with the problem of sampled-data synchronization for complex dynamical networks (CDNs) with time-varying coupling delay and random coupling strengths. The random coupling strengths are described by normal distribution. The sampling period considered here is assumed to be less than a given bound. By taking the characteristic of sampled-data system into account, a discontinuous Lyapunov functional is constructed, and a delay-dependent mean square synchronization criterion is derived. Based on the proposed condition, a set of desired sampled-data controllers are designed in terms of linear matrix inequalities (LMIs) that can be solved effectively by using MATLAB LMI Toolbox. Numerical examples are given to demonstrate the effectiveness of the proposed scheme.

Fundamentally Distributed Information Processing Integrates the Motor Network into the Mental Workspace during Mental Rotation

2016 ◽  
Vol 28 (8) ◽  
pp. 1139-1151
Author(s):  
Alexander Schlegel ◽  
Dedeepya Konuthula ◽  
Prescott Alexander ◽  
Ethan Blackwood ◽  
Peter U. Tse
Keyword(s):  
Information Processing ◽  
Mental Rotation ◽  
Network Activity ◽  
Core Network ◽  
Distributed Information ◽  
Mental Rotations ◽  
Motor Network ◽  
Manual Rotation ◽  
Subcortical Regions ◽  
Increased Activity

The manipulation of mental representations in the human brain appears to share similarities with the physical manipulation of real-world objects. In particular, some neuroimaging studies have found increased activity in motor regions during mental rotation, suggesting that mental and physical operations may involve overlapping neural populations. Does the motor network contribute information processing to mental rotation? If so, does it play a similar computational role in both mental and manual rotation, and how does it communicate with the wider network of areas involved in the mental workspace? Here we used multivariate methods and fMRI to study 24 participants as they mentally rotated 3-D objects or manually rotated their hands in one of four directions. We find that information processing related to mental rotations is distributed widely among many cortical and subcortical regions, that the motor network becomes tightly integrated into a wider mental workspace network during mental rotation, and that motor network activity during mental rotation only partially resembles that involved in manual rotation. Additionally, these findings provide evidence that the mental workspace is organized as a distributed core network that dynamically recruits specialized subnetworks for specific tasks as needed.

scholarly journals The trend of disruption in the functional brain network topology of Alzheimer’s disease

2021 ◽  
Author(s):  
Alireza Fathian ◽  
Yousef Jamali ◽  
Mohammad Reza Raoufy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Small World ◽  
Brain Network ◽  
Cognitively Normal ◽  
Functional Brain ◽  
Rich Club ◽  
Functional Brain Network ◽  
Progressive Disorder

Abstract Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study analyzed the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression, and these trends behaved differently at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, The methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

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