Theoretical Model for Mesoscopic-Level Scale-Free Self-Organization of Functional Brain Networks

Piersa, Jarosław; Piekniewski, Filip; Schreiber, Tomasz

doi:10.1109/tnn.2010.2066989

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…Here, the thermodynamic coupling refers that a flux J occurs without its primary thermodynamic driving force X, or opposite to the direction imposed by the driving force. This is consistent with the second law that states that a finite amount of organization may be obtained at the expense of a greater amount of disorganization in a series of coupled spontaneous processes [16,48,49]. The information processed in the thermodynamic coupling is the functional information, which in turn may lower the total entropy [32].…”

Section: Thermodynamically Coupled Biological Systems and Informationsupporting

confidence: 88%

Information in Biological Systems and the Fluctuation Theorem

Demi̇rel

2014

Entropy

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Some critical trends in information theory, its role in living systems and utilization in fluctuation theory are discussed. The mutual information of thermodynamic coupling is incorporated into the generalized fluctuation theorem by using information theory and nonequilibrium thermodynamics. Thermodynamically coupled dissipative structures in living systems are capable of degrading more energy, and processing complex information through developmental and environmental constraints. The generalized fluctuation theorem can quantify the hysteresis observed in the amount of the irreversible work in nonequilibrium regimes in the presence of information and thermodynamic coupling.

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Section: Thermodynamically Coupled Biological Systems and Informationsupporting

confidence: 88%

Information in Biological Systems and the Fluctuation Theorem

Demi̇rel

2014

Entropy

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“…There is evidence that on a larger scale (if individual neurons are not considered), or for functionally-defined networks, brain connectivity patterns can be considered scale-free [42]–[44]. Researchers have failed to show scale-free architecture in neural network structure [17], [38], although in some cases, [45] a truncated power-law for degree distribution was found.…”

Section: Discussionmentioning

confidence: 99%

Graph Theoretical Model of a Sensorimotor Connectome in Zebrafish

et al. 2012

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Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.

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“…Among them, the modeling of neuronal networks of a brain can be viewed as a typical application of complex networks [2], [3]. In [4]- [6], the theoretical modeling, tackling learning of neuronal networks and the applications of neuronal networks to image processing have been investigated, respectively. Modern brain mapping approaches such as diffusion MRI, functional MRI, EEG, and MEG have constantly produced large datasets of anatomical and functional connection patterns.…”

Section: Introductionmentioning

confidence: 99%

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

Tang

Wang

Gao

et al. 2014

IEEE Trans. Cybern.

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Abstract-Control gains play an important role in the control of a natural or technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible solutions as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that, as the constraint becomes more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide insightful lights on developing new techniques to control a realistic complex network efficiently.

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Theoretical Model for Mesoscopic-Level Scale-Free Self-Organization of Functional Brain Networks

Cited by 16 publications

References 33 publications

Information in Biological Systems and the Fluctuation Theorem

Information in Biological Systems and the Fluctuation Theorem

Graph Theoretical Model of a Sensorimotor Connectome in Zebrafish

On Controllability of Neuronal Networks With Constraints on the Average of Control Gains

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