How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. We compute the integrated information for both, the system’s attractor states, as well as non-stationary dynamical states of the network. We then apply this formalism to brain networks to compute the integrated information for the human brain’s connectome. Compared to a randomly re-wired network, we find that the specific topology of the brain generates greater information complexity.

 

The global dynamical complexity of the human brain network
Xerxes D. Arsiwalla and Paul F. M. J. Verschure

Source: appliednetsci.springeropen.com

Multistable systems exhibit a rich front dynamics between equilibria. In one-dimensional scalar gradient systems, the spread of the fronts is proportional to the energy difference between equilibria. Fronts spreading proportionally to the energetic difference between equilibria is a characteristic of one-dimensional scalar gradient systems. Based on a simple nonvariational bistable model, we show analytically and numerically that the direction and speed of front propagation is led by nonvariational dynamics. We provide experimental evidence of nonvariational front propagation between different molecular orientations in a quasi-one-dimensional liquid-crystal light valve subjected to optical feedback. Free diffraction length allows us to control the variational or nonvariational nature of this system. Numerical simulations of the phenomenological model have quite good agreement with experimental observations.

 

Nonvariational mechanism of front propagation: Theory and experiments
A. J. Alvarez-Socorro, M. G. Clerc, G. González-Cortés, and M. Wilson
Phys. Rev. E 95, 010202(R) – Published 17 January 2017

Source: journals.aps.org

There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible.

 

Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties
Leonardo Zapata-Fonseca, Dobromir Dotov, Ruben Fossion, and Tom Froese

Front. Psychol., 12 December 2016 | https://doi.org/10.3389/fpsyg.2016.01940

Source: journal.frontiersin.org

One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment 1) and the abstractness rating of nouns (Experiment 2).

 

A Novel Procedure for Measuring Semantic Synergy
Yair Neuman, Yiftach Neuman, and Yochai Cohen

Complexity
Volume 2017 (2017), Article ID 5785617, 8 pages
https://doi.org/10.1155/2017/5785617

Source: www.hindawi.com

  A characteristic property of networks is their ability to propagate
influences, such as infectious diseases, behavioral changes, and failures. An
especially important class of such contagious dynamics is that of cascading
processes. These processes include, for example, cascading failures in
infrastructure systems, extinctions cascades in ecological networks, and
information cascades in social systems. In this review, we discuss recent
progress and challenges associated with the modeling, prediction, detection,
and control of cascades in networks.

 

The Unfolding and Control of Network Cascades,
Adilson E. Motter and Yang Yang,
Physics Today, January 2017, page 32.
http://physicstoday.scitation.org/doi/10.1063/PT.3.3426

Source: physicstoday.scitation.org