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

Integrated information, a measure proposed by Integrated Information Theory as a correlate of conscious experience, behaves as an order parameter that undergoes a phase transition at the critical point in generalized Ising models of small neural networks. In simulations of 159 random, positively weighted five-node excitatory networks, integrated information peaked at the critical temperature, where its generalized susceptibility was maximal. At this point, the system was maximally receptive and responsive to perturbations of its own states. The findings show that integrated information can capture critical behavior in an empirical dataset derived from the generalized Ising model.

Study at a glance

Abstract

Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ 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 Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.