The Emergence of Integrated Information, Complexity, and 'Consciousness' at Criticality.

Nicholas J M Popiel,Francesco Riganello,Sina Khajehabdollahi,Adrian M Owen,Pubuditha M Abeyasinghe,Andrea Soddu,Emily S Nichols

doi:10.3390/e22030339

Nicholas J M Popiel, Francesco Riganello + Show 5 more

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https://doi.org/10.3390/e22030339

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Abstract

Integrated Information Theory (IIT) posits that integrated information () represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate as a function of temperature in toy models of fully connected neural networks. A Monte–Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.

Highlights

A growing body of evidence has emerged suggesting that many disparate natural, and biological, phenomena reside in a critical regime of dynamics on the cusp between order and disorder [1,2,3,4,5,6,7,8,9,10,11]
Results indicated that the integrated conceptual information generated using the generalized Ising model, much like the classical variable magnetization, underwent a phase transition at the critical temperature
This was detected by locating the peaks of its susceptibility curves as a function of temperature [41], indicating that the integrated information structure of simple neural networks behaves critically, exhibiting maximal susceptibility to perturbations and allowing for a form of consciousness that balances coherence and continuity with information and variance

Summary

Introduction

A growing body of evidence has emerged suggesting that many disparate natural, and biological, phenomena reside in a critical regime of dynamics on the cusp between order and disorder [1,2,3,4,5,6,7,8,9,10,11]. Demonstrated that information is maximized at the critical temperature in an Ising scheme using human connectome data and beyond criticality, a law of marginal diminishing returns is reached [15]. These ideas have been further developed to suggest more broadly that critical systems are evolutionarily. Integrated information theory (IIT) is a top-down, phenomenological approach to defining consciousness [21]. The main measure proposed by IIT 3.0 is the mathematical entity called integrated conceptual information (Φ) (Big Phi) which generally seeks to measure ‘how much the whole is greater than the sum of its parts’ of the causal structure being studied [21]. Though other measures exist [22] which attempt to capture some form of integration or complexity, this paper uses

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