Characterizing the complexity of brain and mind networks

Abstract

Recent studies of brain connectivity and language with methods of complex networks have revealed common features of organization. These observations open a window to better understand the intrinsic relationship between the brain and the mind by studying how information is either physically stored or mentally represented. In this paper, we review some of the results in both brain and linguistic networks, and we illustrate how modelling approaches can serve to comprehend the relationship between the structure of the brain and its function. On the one hand, we show that brain and neural networks display dynamical behaviour with optimal complexity in terms of a balance between their capacity to simultaneously segregate and integrate information. On the other hand, we show how principles of neural organization can be implemented into models of memory storage and recognition to reproduce spontaneous transitions between memories, resembling phenomena of memory association studied in psycholinguistic experiments.

Increment Entropy as a Measure of Complexity for Time Series

“…Nowadays, the notion of complexity has been ubiquitously used to examine a variety of time series, ranging from diverse physiological signals [1][2][3][4][5][6][7][8][9] to financial time series [10,11] and ecological time series [12]. There there is not an established universal definition of complexity to date [13].…”

Are scientific memes inherited differently from gendered authorship?

“…A possible reason for such a simple relation between the relative frequencies and propagation scores of scientific memes may rely on the fact that the less frequent ones are presumably more informative and therefore occur less often. Likewise, functional words -such as determiners and prepositionscarrying less meaning, occur very frequently and therefore occupy the most central positions in linguistic (co-occurrence) networks ( [69], [70]).…”

The sleeping brain as a complex system

“…The foundation of this approach is based on the initial observation that large classes of systems can be represented by networks that share universal features such as degree distribution according to a power law, the so-called 'scale-free' graphs, the small-world property and a modular structure. The contribution of Zamora-López et al [30] reviews some of these findings for brain and mind networks. In brain networks, the nodes are neurons, groups of neurons or cortical regions, and the edges represent anatomical connections (structural brain networks) or dynamical correlations during particular tasks or states (functional brain networks).…”

“…Examples are memory networks or linguistic networks, with words being the nodes and associations or co-occurrences represented by the edges. Zamora-López et al [30] point out that both brain and mind networks show (i) a broad distribution of node degrees, with some nodes being hubs, (ii) small-world properties and (iii) organization into modular and hierarchical structures.…”

“…An influential approach in this field is the idea that the complexity of the functional brain network can be related to the level of consciousness in the corresponding vigilance state [31], as formulated in the information integration theory of consciousness [29]. Using a complexity measure for networks that can be related to one of the early measures used by Tononi et al [32], Zamora-López et al [30] show that the complexity of brain networks depends essentially on the existence of hubs and their modular structure. In this issue, Spoormaker et al [33] highlight changes in large-scale functional brain networks between the awake state, light sleep and deep sleep.…”