scholarly journals Graph theory in higher order topological analysis of urban scenes

2007 ◽  
Vol 31 (4) ◽  
pp. 426-440 ◽  
Author(s):  
J.-P. de Almeida ◽  
J.G. Morley ◽  
I.J. Dowman
Author(s):  
Thomas F. Varley ◽  
Vanessa Denny ◽  
Olaf Sporns ◽  
Alice Patania

AbstractResearch into the neural correlates of consciousness has found that the vividness and complexity of conscious experience is related to the structure of brain dynamics, and that alterations to consciousness track changes in temporal evolution of brain states. Despite inducing externally similar states, propofol and ketamine produce different subjective states of consciousness: here we explore the different effects of these two anaesthetics on the structure of dynamical attractors reconstructed from electrophysiological activity recorded from cerebral cortex of two non-human primates. We used two different methods of attractor reconstruction: the first embeds the recordings in a continuous high-dimensional manifold on which we use topological data analysis to infer the presence (or absence) of higher-order dynamics. The second reconstruction, an ordinal partition network embedding, allows us to create a discrete state-transition network approximation of a continuous attractor, which is amenable to information-theoretic analysis and contains rich information about state-transition dynamics. We find that the awake condition generally had the “richest” structure, with the widest repertoire of available states, the presence of pronounced higher-order structures, and the least deterministic dynamics. In contrast, the propofol condition had the most dissimilar dynamics to normal consciousness, transitioning to a more impoverished, constrained, low-structure regime. The ketamine condition, interestingly, seemed to combine aspects of both: while it was generally less complex than the awake condition, it remained well above propofol in almost all measures. These results may provides insights into how consciousness can persist under the influence of ketamine and the battery of measures used provides deeper and more comprehensive insights than what is typically gained by using point-measures of complexity.


eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Matthew DB Jackson ◽  
Hao Xu ◽  
Salva Duran-Nebreda ◽  
Petra Stamm ◽  
George W Bassel

Multicellularity arose as a result of adaptive advantages conferred to complex cellular assemblies. The arrangement of cells within organs endows higher-order functionality through a structure-function relationship, though the organizational properties of these multicellular configurations remain poorly understood. We investigated the topological properties of complex organ architecture by digitally capturing global cellular interactions in the plant embryonic stem (hypocotyl), and analyzing these using quantitative network analysis. This revealed the presence of coherent conduits of reduced path length across epidermal atrichoblast cell files. The preferential movement of small molecules along this cell type was demonstrated using fluorescence transport assays. Both robustness and plasticity in this higher order property of atrichoblast patterning was observed across diverse genetic backgrounds, and the analysis of genetic patterning mutants identified the contribution of gene activity towards their construction. This topological analysis of multicellular structural organization reveals higher order functions for patterning and principles of complex organ construction.


2021 ◽  
Vol 8 (6) ◽  
pp. 201971
Author(s):  
Thomas F. Varley ◽  
Vanessa Denny ◽  
Olaf Sporns ◽  
Alice Patania

Research has found that the vividness of conscious experience is related to brain dynamics. Despite both being anaesthetics, propofol and ketamine produce different subjective states: we explore the different effects of these two anaesthetics on the structure of dynamic attractors reconstructed from electrophysiological activity recorded from cerebral cortex of two macaques. We used two methods: the first embeds the recordings in a continuous high-dimensional manifold on which we use topological data analysis to infer the presence of higher-order dynamics. The second reconstruction, an ordinal partition network embedding, allows us to create a discrete state-transition network, which is amenable to information-theoretic analysis and contains rich information about state-transition dynamics. We find that the awake condition generally had the ‘richest’ structure, visiting the most states, the presence of pronounced higher-order structures, and the least deterministic dynamics. By contrast, the propofol condition had the most dissimilar dynamics, transitioning to a more impoverished, constrained, low-structure regime. The ketamine condition, interestingly, seemed to combine aspects of both: while it was generally less complex than the awake condition, it remained well above propofol in almost all measures. These results provide deeper and more comprehensive insights than what is typically gained by using point-measures of complexity.


2012 ◽  
Vol 84 (4) ◽  
pp. 1069-1088
Author(s):  
Michał K. Cyrański ◽  
Arkadiusz Ciesielski ◽  
Tadeusz M. Krygowski ◽  
Dorota K. Stępień

Application of topological analysis and graph theory to benzenoid hydrocarbons leads to the determination of fundamentals of aromaticity: the Hückel rule and the Clar rule. The approach, based on a treatment of the adjacency matrix, allows resonance energy (RE)-like characteristics to be estimated with quite good accuracy, and magnetic aromaticity indices to be derived for both the individual rings and the whole molecules. It also allows an effective approach for interpreting ring current formation in molecules when exposed to an external magnetic field. The transformation of the perturbation matrix into a form describing the canonical structures allows their gradation and determination of their stabilizing/destabilizing character.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Farukh Ejaz ◽  
Muhammad Hussain ◽  
Hamad Almohamedh ◽  
Khalid M. Alhamed ◽  
Rana Alabdan ◽  
...  

Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices.


2021 ◽  
Author(s):  
Oana Carja ◽  
Yang Ping Kuo

To design population topologies that can accelerate rates of solution discovery in directed evolution problems or in evolutionary optimization applications, we must first systematically understand how population structure shapes evolutionary outcome. Using the mathematical formalism of evolutionary graph theory, recent studies have shown how to topologically build networks of population interaction that increase probabilities of fixation of beneficial mutations, at the expense, however, of longer fixation times, which can slow down rates of evolution under elevated mutation rate. Here we find that moving beyond dyadic interactions is fundamental to explain the trade-offs between probability and time to fixation. We show that higher-order motifs, and in particular three-node structures, allow tuning of times to fixation, without changes in probabilities of fixation. This gives a near-continuous control over achieving solutions that allow for a wide range of times to fixation. We apply our algorithms and analytic results to two evolutionary optimization problems and show that the rate at which evolving agents learn to navigate their environment can be tuned near continuously by adjusting the higher-order topology of the agent population. We show that the effects of population structure on the rate of evolution critically depend on the optimization landscape and find that decelerators, with longer times to fixation of new mutants, are able to reach the optimal solutions faster than accelerators in complex solution spaces. Our results highlight that no one population topology fits all optimization applications, and we provide analytic and computational tools that allow for the design of networks suitable for each specific task.


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