Fisher Information at the Edge of Chaos in Random Boolean Networks

Wang, X. Rosalind; Lizier, Joseph T.; Prokopenko, Mikhail

doi:10.1162/artl_a_00041

Cited by 52 publications

(58 citation statements)

References 26 publications

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“…This framework is called local information dynamics and has been successfully applied to unravel computation in swarms (Wang et al, 2011), in Boolean networks (Lizier et al, 2011b), and in neural models (Boedecker et al, 2012) and data (Wibral et al, 2014a) (also see Section 6 for details on these example applications).…”

Section: Analyzing Neural Coding and Goal Functions In A Domain-indepmentioning

confidence: 99%

Bits from Brains for Biologically Inspired Computing

2015

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Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification -locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.

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Section: Analyzing Neural Coding and Goal Functions In A Domain-indepmentioning

confidence: 99%

Bits from Brains for Biologically Inspired Computing

2015

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“…Their results suggest that theoretically, we can predict critical thresholds in epidemics [14], which could be of a substantial value in health care. In practice, one of the challenges lies in pinpointing such critical thresholds in finite-size systems, where a precise identification of phase transitions requires an estimation of the rate of change of the order parameter, often from finite and/or distributed data [15,16].…”

Section: Introductionmentioning

confidence: 99%

Criticality and Information Dynamics in Epidemiological Models

Erten

Lizier

Piraveenan

et al. 2017

Entropy

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Abstract:Understanding epidemic dynamics has always been a challenge. As witnessed from the ongoing Zika or the seasonal Influenza epidemics, we still need to improve our analytical methods to better understand and control epidemics. While the emergence of complex sciences in the turn of the millennium have resulted in their implementation in modelling epidemics, there is still a need for improving our understanding of critical dynamics in epidemics. In this study, using agent-based modelling, we simulate a Susceptible-Infected-Susceptible (SIS) epidemic on a homogeneous network. We use transfer entropy and active information storage from information dynamics framework to characterise the critical transition in epidemiological models. Our study shows that both (bias-corrected) transfer entropy and active information storage maximise after the critical threshold (R 0 = 1). This is the first step toward an information dynamics approach to epidemics. Understanding the dynamics around the criticality in epidemiological models can provide us insights about emergent diseases and disease control.

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“…We set J = 1 and k B = 2, for which T c ≈ 2.269 J k B ≈ 1.13 (the first analytical result on the phase transition in the 2D Ising model has been presented by Onsager [15]). The values of T span the range [1,2] at steps of 0.05. For each value of T, 10 runs are performed starting from random initial conditions chosen with −1 spin probability equal to 0.25, so as to start with an intermediate condition between µ = −1 and µ = 0 (we also ran experiments with different biases in the initial condition and did not observe any difference in the results).…”

Section: Results On the Ising Modelmentioning

confidence: 99%

Identifying Critical States through the Relevance Index

Roli

Villani

Caprari

et al. 2017

Entropy

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Abstract:The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality.

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Fisher Information at the Edge of Chaos in Random Boolean Networks

Cited by 52 publications

References 26 publications

Bits from Brains for Biologically Inspired Computing

Bits from Brains for Biologically Inspired Computing

Criticality and Information Dynamics in Epidemiological Models

Identifying Critical States through the Relevance Index

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