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Information decomposition and the informational architecture of the brain

Luppi, Andrea I., Rosas, Fernando E., Mediano, Pedro A.M., Menon, David K., Stamatakis, Emmanuel A.·2024·Trends in Cognitive Sciences
DOI 10.1016/j.tics.2023.11.005
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Figure 2 Information decomposition provides a unifying framework to resolve conceptual tensions in cognitive science. Each arrow across the central triangle represents an axis of dichotomy in the cognitive science and neuroscience literature. Each axis has one end corresponding to one type of information, but at the other end it conflates two distinct types of information, giving rise to apparent contradictions. As outlined in the main text, 'integration' conflates synergy (integration-as-cooperation) and redundancy (integration-as-oneness). 'Differentiation' conflates the independence of unique information, and the complementarity of synergy. Additionally, the term 'local' is ambiguous between redundant and unique information: when an individual source carries unique or redundant information, all such information is available locally (i.e., from that source): it can be fully obtained from that source alone. Unlike unique information, however, redundant information is multiply -localised, because it is available from any of several individual sources. Synergistic information is instead de -localised: it cannot be obtained from any individual source. These tensions can be resolved by carefully distinguishing different information types.
Figure 2 Information decomposition provides a unifying framework to resolve conceptual tensions in cognitive science. Each arrow across the central triangle represents an axis of dichotomy in the cognitive science and neuroscience literature. Each axis has one end corresponding to one type of information, but at the other end it conflates two distinct types of information, giving rise to apparent contradictions. As outlined in the main text, 'integration' conflates synergy (integration-as-cooperation) and redundancy (integration-as-oneness). 'Differentiation' conflates the independence of unique information, and the complementarity of synergy. Additionally, the term 'local' is ambiguous between redundant and unique information: when an individual source carries unique or redundant information, all such information is available locally (i.e., from that source): it can be fully obtained from that source alone. Unlike unique information, however, redundant information is multiply -localised, because it is available from any of several individual sources. Synergistic information is instead de -localised: it cannot be obtained from any individual source. These tensions can be resolved by carefully distinguishing different information types.CC-BY · From the PDF
Figure 3 Synergy and redundancy in the human brain. a | Relative prevalence of synergy and redundancy in the human brain delineates a unimodal-transmodal synergy-redundancy axis. Redundancy (blue) is associated with primary sensory and motor functions; it exhibits a highly modular network organisation, being higher within than between intrinsic connectivity networks (ICNs); it is coupled to the underlying structural connectivity. Synergy (red) is associated with complex cognition; it is greater between regions that belong to different ICNs; and it is associated with synaptic density and synapse- and dendrite-related genes and metabolic processes. b | Schematic account of evolutionary differences in synergy between humans and other primates. Whereas redundancy is stable between macaques and humans, the overall proportion of information that is carried synergistically is significantly greater in humans. Since the high-synergy regions are also the most evolutionarily expanded, we speculate that cortical expansion may be responsible for the additional synergy observed in the human brain - and in turn, for humans' greater cognitive capacities.
Figure 3 Synergy and redundancy in the human brain. a | Relative prevalence of synergy and redundancy in the human brain delineates a unimodal-transmodal synergy-redundancy axis. Redundancy (blue) is associated with primary sensory and motor functions; it exhibits a highly modular network organisation, being higher within than between intrinsic connectivity networks (ICNs); it is coupled to the underlying structural connectivity. Synergy (red) is associated with complex cognition; it is greater between regions that belong to different ICNs; and it is associated with synaptic density and synapse- and dendrite-related genes and metabolic processes. b | Schematic account of evolutionary differences in synergy between humans and other primates. Whereas redundancy is stable between macaques and humans, the overall proportion of information that is carried synergistically is significantly greater in humans. Since the high-synergy regions are also the most evolutionarily expanded, we speculate that cortical expansion may be responsible for the additional synergy observed in the human brain - and in turn, for humans' greater cognitive capacities.CC-BY · From the PDF
Figure I. Visual representation of information decomposition with two source variables. Redundancy is a subset of both the MI between X1 and Y, and the MI between X2 and Y. Unique information is the non-redundant part of each MI. Synergy is not contained in either MI, but depends on both.
Figure I. Visual representation of information decomposition with two source variables. Redundancy is a subset of both the MI between X1 and Y, and the MI between X2 and Y. Unique information is the non-redundant part of each MI. Synergy is not contained in either MI, but depends on both.CC-BY · From the PDF
Figure I. Information decomposition of Transfer Entropy and Active Information Storage reveals their partial overlap due to information duplication. Rows indicate how the two sources carried information at t, and columns indicate how they carry the information at t+1. TE from X to Y (red circles) includes all information that was not present in Y at t, and is present in Y at t+1. This includes information that was uniquely provided by X at t, and is redundantly provided by both X and Y at t+1 (i.e., duplication of information; violet circle). AIS within X (blue circles) comprises information that was present in X at t, and is also present in X at t+1. This also includes the duplication of information from X to X and Y, which is therefore shared by TE and AIS.
Figure I. Information decomposition of Transfer Entropy and Active Information Storage reveals their partial overlap due to information duplication. Rows indicate how the two sources carried information at t, and columns indicate how they carry the information at t+1. TE from X to Y (red circles) includes all information that was not present in Y at t, and is present in Y at t+1. This includes information that was uniquely provided by X at t, and is redundantly provided by both X and Y at t+1 (i.e., duplication of information; violet circle). AIS within X (blue circles) comprises information that was present in X at t, and is also present in X at t+1. This also includes the duplication of information from X to X and Y, which is therefore shared by TE and AIS.CC-BY · From the PDF
Figure I. Using information types as a Rosetta Stone to relate the structure and function of biological and artificial systems. In the biological brain, information dynamics can shed light on the relationship between the structural and functional organisation of the brain, and cognitive and behavioural variables (for both humans and other species). In artificial systems, information dynamics can likewise illuminate the relationship between the system's architecture and its computational properties and performance. Because information dynamics are substrate-independent, they can be compared across humans, non-human biological systems, and artificial cognitive systems, providing a common language. Figure adapted in part from [49], originally published under CC-BY license, and with permission from Margulies et al (2016) [141].
Figure I. Using information types as a Rosetta Stone to relate the structure and function of biological and artificial systems. In the biological brain, information dynamics can shed light on the relationship between the structural and functional organisation of the brain, and cognitive and behavioural variables (for both humans and other species). In artificial systems, information dynamics can likewise illuminate the relationship between the system's architecture and its computational properties and performance. Because information dynamics are substrate-independent, they can be compared across humans, non-human biological systems, and artificial cognitive systems, providing a common language. Figure adapted in part from [49], originally published under CC-BY license, and with permission from Margulies et al (2016) [141].CC-BY · From the PDF

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Metadata

Authors
Luppi, A. I., Rosas, F. E., Mediano, P. A. M., Menon, D. K., & Stamatakis, E. A.
ORCID
Stamatakis, Emmanuel A.
Venue
Trends in Cognitive Sciences
Publisher
Elsevier BV
Published
2024-04
Volume
28
Issue
4
Pages
352-368
ISSN
1364-6613
DOI
10.1016/j.tics.2023.11.005
Cited by
140
References
10
Language
en
Licence
Funders
  • Royal College of Anaesthetists
  • Canadian Institute for Advanced Research (RCZB/072 RG93193)
  • Fonds de recherche du Québec – Nature et technologies (2020-RS4-265502)
  • McGill University
  • UCLH Biomedical Research Centre
  • National Institute for Health and Care Research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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