The integrated information Φ of an integrate and fire network.

Integrated Information Theory (IIT) proposes that consciousness arises from a system's ability to integrate information. Applying IIT to a simulated network of integrate-and-fire (IAF) neurons shows that such a network can have a non-zero Φ value—a measure of integrated information—under specific conditions. The complexity of the network's dynamics does not necessarily correlate with its Φ value. However, the amount of integrated information increases with the neurons' time constant, reflecting their integrative capacity. The integrated information measure defined in IIT 3.0 is not resilient to noise when the network includes internal random fluctuations.

Study at a glance

Abstract

Integrated Information Theory is a theoretical framework proposing that consciousness is a fundamental property of systems capable of integrating information. To bridge the gap between the theoretical concept and the practical use in actual neurobiological systems, we have applied the Integrated Information Theory approach to a simulated network of integrate and fire neurons (IAF). The primary contribution of this study is several empirical findings. Our analysis shows that such a network can possess a non-zero Φ value under certain conditions and parameter settings. Additionally, our research indicates that the complexity of the network's dynamics doesn't necessarily correlate with its Φ value. On the other hand, the quantity of integrated information within the network appears to grow with the IAF neurons' time constant, which reflects their integrative capacity. Furthermore, our examination of the integrate and fire network with internal random fluctuations demonstrates that the integrated information measure, as defined in IIT version 3.0, is not resilient to noise.