Criticality and Information Dynamics in Epidemiological Models

Erten, Emre; Lizier, Joseph T.; Piraveenan, Mahendra; Prokopenko, Mikhail

doi:10.3390/e19050194

Cited by 37 publications

(42 citation statements)

References 44 publications

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“…Many researchers, therefore, have considered information transfer (or causal relationships) among individuals in small groups [37,38,39]. The (local) transfer entropy is the preferred measure to use in this case [40,41,42,43,44,45]. For example, Crosato et al [39] showed that the transfer of misinformation happens in five-fish school when the whole school changes direction.…”

Section: Introductionmentioning

confidence: 99%

Finding continuity and discontinuity in fish schools via integrated information theory

et al. 2020

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Collective behaviour is known to be the result of diverse dynamics and is sometimes likened to a living system. Although many studies have revealed the dynamics of various collective behaviours, their main focus was on the information process inside the collective, not on the whole system itself. For example, the qualitative difference between two elements and three elements as a system has rarely been investigated. Tononi et al. have proposed Integrated Information Theory (IIT) to measure the degree of consciousness Φ. IIT postulates that the amount of information loss caused by certain partitions is equivalent to the degree of information integration in the system. This measure is not only useful for estimating the degree of consciousness but can also be applied to more general network systems. Here we applied IIT (in particular, IIT 3.0 using PyPhi) to analyse real fish schools (Plecoglossus altivelis). Our hypothesis in this study is a very simple one: a living system evolves to raise its Φ value. If we accept this hypothesis, IIT reveals the existence of continuous and discontinuous properties as group size varies. For example, leadership in the fish school emerged for a school size of four or above; but not below three. Furthermore, this transition was not observed by measuring mutual information or in a simple Boids model. This result suggests that integrated information Φ can reveal some inherent properties which cannot be observed using other measures. We also discuss how the fish recognition of the figure-ground relation, that is, what determines the relevant ON and OFF states, may reveal various optimal paths for obtaining the functional evolution of collective behaviour.

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Section: Introductionmentioning

confidence: 99%

Finding continuity and discontinuity in fish schools via integrated information theory

et al. 2020

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“…The determination of these embedding parameters followed the method of Garland et al [79] finding the values which maximize the AIS, with the important additional inclusion of bias correction (because increasing k generally serves to increase bias of the estimate) [80]. For several sample σ, γ pairs in both the sub-and supercritical regimes we examined these parameter choices across all regions (up to k, τ ≤ 30), and found the optimal choices to be consistently close to k = 25 and τ = 12 for all variables (for the sampling interval ∆t = 0.5 ms).…”

Section: Active Information Storagementioning

confidence: 99%

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Han

Aburn

et al. 2019

Preprint

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A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a 'critical' transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain. Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find March 11, 2019 1/26 that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition. IntroductionAlthough there is a long history relating individual brain regions to specific and specialized functions, regions in isolation cannot perform meaningful physiological or cognitive processes [1]. Instead, starting at a lower scale, a few prominent features of ...

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“…Code used to generate the data used in this paper can be found at https://github.com/NathanHarding/Thermeff-cont. u = log 10 (ν) (A1) so that du dν = 1 ν log 10 = 10 −u log (10) .…”

Section: Data Accessibilitymentioning

confidence: 99%

Thermodynamic efficiency of contagions: a statistical mechanical analysis of the SIS epidemic model

2018

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We present a novel approach to the study of epidemics on networks as thermodynamic phenomena, quantifying the thermodynamic efficiency of contagions, considered as distributed computational processes. Modelling SIS dynamics on a contact network statistical-mechanically, we follow the Maximum Entropy principle to obtain steady state distributions and derive, under certain assumptions, relevant thermodynamic quantities both analytically and numerically. In particular, we obtain closed form solutions for some cases, while interpreting key epidemic variables, such as the reproductive ratio R0 of a SIS model, in a statistical mechanical setting. On the other hand, we consider configuration and free entropy, as well as the Fisher Information, in the epidemiological context. This allowed us to identify criticality and distinct phases of epidemic processes. For each of the considered thermodynamic quantities, we compare the analytical solutions informed by the Maximum Entropy principle with the numerical estimates for SIS epidemics simulated on Watts-Strogatz random graphs.

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Criticality and Information Dynamics in Epidemiological Models

Cited by 37 publications

References 44 publications

Finding continuity and discontinuity in fish schools via integrated information theory

Finding continuity and discontinuity in fish schools via integrated information theory

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

Thermodynamic efficiency of contagions: a statistical mechanical analysis of the SIS epidemic model

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