Elsevier

NeuroImage

Volume 108, March 2015, Pages 292-300
NeuroImage

Connectivity in the human brain dissociates entropy and complexity of auditory inputs

https://doi.org/10.1016/j.neuroimage.2014.12.048Get rights and content
Under a Creative Commons license
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Highlights

  • Complexity science holds that highly ordered and random signals have low complexity.

  • We examine whether effective connectivity tracks both stimulus entropy and complexity.

  • Connectivity tracked entropy both linearly and via an inverse U-shaped profile.

  • Both profiles were identified for hippocampal and anterior cingulate networks.

Abstract

Complex systems are described according to two central dimensions: (a) the randomness of their output, quantified via entropy; and (b) their complexity, which reflects the organization of a system's generators. Whereas some approaches hold that complexity can be reduced to uncertainty or entropy, an axiom of complexity science is that signals with very high or very low entropy are generated by relatively non-complex systems, while complex systems typically generate outputs with entropy peaking between these two extremes. In understanding their environment, individuals would benefit from coding for both input entropy and complexity; entropy indexes uncertainty and can inform probabilistic coding strategies, whereas complexity reflects a concise and abstract representation of the underlying environmental configuration, which can serve independent purposes, e.g., as a template for generalization and rapid comparisons between environments. Using functional neuroimaging, we demonstrate that, in response to passively processed auditory inputs, functional integration patterns in the human brain track both the entropy and complexity of the auditory signal. Connectivity between several brain regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty, whereas connectivity between other regions tracked entropy in a convex manner consistent with sensitivity to input complexity. These findings suggest that the human brain simultaneously tracks the uncertainty of sensory data and effectively models their environmental generators.

Keywords

Complexity
Simplicity
Entropy
Generative model
Prediction
Uncertainty

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This work was supported by the European Research Council under the 7th framework starting grant program (European Research Council Starting Grant no. 263318 to U.H.).