Scale invariance in natural and artificial collective systems: a review

Khaluf, Yara; Ferrante, Eliseo; Simoens, Pieter; Huepe, Cristián

doi:10.1098/rsif.2017.0662

Cited by 51 publications

(52 citation statements)

References 222 publications

(227 reference statements)

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“…Geometry and topology can be investigated across multiple scales of any complex system or its emergent dynamics [59]. While some systems can display heterogeneity in geometric principles across spatial and temporal scales, others display greater scale-invariance, with the principles at one scale being recapitulated at other scales [60]. Applying our methods at different scales, we find that neural activity patterns elicited by value judgments of learned stimuli display similar geometric principles whether assessed at the level of the whole brain, or at the level of multivoxel patterns in single brain areas.…”

Section: Changes In Neural Representations During Learning and Practicementioning

confidence: 99%

Effective learning is accompanied by high-dimensional and efficient representations of neural activity

Tang¹,

Mattar²,

Giusti³

et al. 2019

Nat Neurosci

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A fundamental cognitive process is the ability to map value and identity onto objects as we learn about them. Exactly how such mental constructs emerge and what kind of space best embeds this mapping remains incompletely understood. Here we develop tools to quantify the space and organization of such a mapping, thereby providing a framework for studying the geometric representations of neural responses as reflected in functional MRI. Considering how human subjects learn the values of novel objects, we show that quick learners have a higher dimensional geometric representation than slow learners, and hence more easily distinguishable whole-brain responses to objects of different value. Furthermore, we find that quick learners display a more compact embedding of their neural responses and hence have a higher ratio of their task-based dimension to their embedding dimension -consistent with a greater efficiency of cognitive coding. Lastly, we investigate the neurophysiological drivers of high dimensional patterns at both regional and voxel levels, and we complete our study with a complementary test of the distinguishability of associated whole-brain responses. Our results demonstrate a spatial organization of neural responses characteristic of learning, and offer a suite of geometric measures applicable to the study of efficient coding in higher-order cognitive processes more broadly.

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Section: Changes In Neural Representations During Learning and Practicementioning

confidence: 99%

Effective learning is accompanied by high-dimensional and efficient representations of neural activity

Tang¹,

Mattar²,

Giusti³

et al. 2019

Nat Neurosci

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show abstract

“…One of the main contributions of this work has been the design of a collective system able to exhibit collective response to environmental changes, in a way that is not only scaleinvariant (Khaluf et al 2017) but that had superior performance as the system scale increased. There are many possible directions for future work.…”

Section: Conclusion Discussion and Future Workmentioning

confidence: 99%

Collective decision making in dynamic environments

2019

Self Cite

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Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options (n = 2). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents' density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation. Keywords Dynamic environments • Collective decision making • Best-of-n • Swarm robotics • Complex adaptive systems B Eliseo Ferrante

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“…Where there is a true power-law with an exponent less than 2, since it is indicative of diverging quantities, there is a tendency to associate such a pattern with a critical phenomenon. However, scale-free patterns can also arise when underlying processes operate at multiple scales [66,67]. Naive association of observed scale-free behaviours with either criticality or stability are both problematic.…”

Section: Resultsmentioning

confidence: 99%

Patchiness and scale-free correlations: characterising criticality in ecosystems

Sankaran¹,

Majumder

Viswanathan

et al. 2017

Preprint

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A variety of ecosystems exhibit spatial clustering devoid of characteristic sizes, also known as scale-free clustering. In physics, scale-free behaviour is known to arise when a system is at a critical point, which occurs at the edge of two phases of matter. Scale-free clustering in physics therefore indicates that a system is not resilient. Spatial ecological studies, however, posit that scale-free clustering arises away from critical points and is therefore an indicator of robustness. This inconsistency is troubling. Here, we synthesize the literature on cluster-size distributions together with analyses of a spatial ecological model that incorporates local birth, death and positive feedbacks. We argue that scalefree clustering in real ecological systems is driven by strong positive feedbacks. Using the model, we demonstrate that power-law relations may occur far away from, near or at the critical point of ecosystem collapse depending on the strength of local positive feedbacks. We therefore infer that clustering patterns are unrelated to critical points of ecosystem collapse. Power-law clustering, instead, indicates a different critical point, called a percolation point, that signifies the onset of spanning clusters in a landscape.Finally, we show that a collapse or a regime shift in an ecosystem is characterized by the emergence of scale-free spatial correlations in the system, reflected in a scale-free powerspectrum. * sumithras@iisc.ac.in 1 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Scale invariance in natural and artificial collective systems: a review

Cited by 51 publications

References 222 publications

Effective learning is accompanied by high-dimensional and efficient representations of neural activity

Effective learning is accompanied by high-dimensional and efficient representations of neural activity

Collective decision making in dynamic environments

Patchiness and scale-free correlations: characterising criticality in ecosystems

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