Measuring information integration

By Giulio Tononi, Olaf Sporns. BMC Neuroscience, 2003

314 citations recorded by the source corpus.

BackgroundTo understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system.ResultsThe capacity to integrate information, or Φ, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Φ is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration).ConclusionBased on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/10141aa39e1626a96bbe51a3c6475523737020b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cognitive Neuroscience

Citations in the corpus

Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, unknown): “…and connectivity (Beckmann and Smith, 2004; Calhoun et al., 2001, 2003); (Bullmore and Sporns, 2009; Cole et al., 2010; Deco et al., 2011; Friston et al., 1997; Greicius et al., 2003; McIntosh, 2000; Rubinov and Sporns, 2010; Sun et al., 2004; Supekar et al., 2008; Tononi and Sporns, 2003).”
Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework (2008, background): “Previous work [2,4,5] focused on neural systems modeled as stationary multidimensional Gaussian processes.”
Motifs in Brain Networks (2004, background): “The importance of a large repertoire of functional circuits for ﬂexible and efﬁcient neural processing has long been recognized (Walter 1953; Ashby 1960) and has recently received renewed theoretical and experimental attention (Tononi et al. 1999; Tononi and Sporns 2003).”
Mapping Information Flow in Sensorimotor Networks (2006, background): “, Bayesian networks [30]), and perturbation analysis [31].”
Conscious Processing and the Global Neuronal Workspace Hypothesis (2020, background): “IIT is primarily a mathematical theory: it introduces a quantity called F, which quantifies the degree of consciousness of any system, biological or artificial, and suggests that at any given moment, the neural correlate of consciousness is a complex yielding a maximum of irreducible, intrinsic caus”
Consciousness as Integrated Information: a Provisional Manifesto (2008, methodology): “Whether certain kinds of random networks (Tononi and Sporns, 2003), or even periodic network such as grids (Balduzzi and Tononi, 2008), could achieve high values of (albeit inefficiently) by simply increasing the number of elements remains to be determined.”
An information integration theory of consciousness (2004, background): “Further simulations show that it is possible to construct a large complex of high Φ by joining smaller complexes through reciprocal connections [8].”
Organization, development and function of complex brain networks. (2004, background): “Such architectures also promote high levels of information integration [55] and the formation of an integrated “dynamic core”, a potential neural correlate of higher cognition and consciousness [56,57].”