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The integrated information Φ of an integrate and fire network
Miłosz Danilczuk1, Marek Pokropski2, Piotr Suffczynski1
1Faculty of Physics, University of Warsaw, Warsaw, Poland.
Plos Computational Biology
|March 9, 2026
Summary
Integrated Information Theory (IIT) was applied to a simulated network of integrate and fire neurons. The study found that consciousness, measured by Φ, can exist in such networks and is influenced by neuron properties and noise.
Area of Science:
- Computational Neuroscience
- Theoretical Neuroscience
- Philosophy of Mind
Background:
- Integrated Information Theory (IIT) posits consciousness as a fundamental property of information-integrating systems.
- Bridging theoretical consciousness frameworks with neurobiological systems requires empirical validation.
- Simulated neural networks offer a platform for testing theoretical concepts like IIT.
Purpose of the Study:
- To apply Integrated Information Theory (IIT) to a simulated network of integrate and fire (IAF) neurons.
- To investigate the conditions under which a non-zero Φ value (a measure of integrated information) can be observed in IAF networks.
- To explore the relationship between network dynamics, neuron properties, and the quantity of integrated information.
Main Methods:
- Development and simulation of a network model composed of integrate and fire (IAF) neurons.
- Application of Integrated Information Theory (IIT) version 3.0 to quantify integrated information (Φ) within the simulated network.
- Analysis of network dynamics, neuron time constants, and the impact of internal random fluctuations (noise) on Φ values.
Main Results:
- The simulated IAF network demonstrated the capacity to possess a non-zero Φ value under specific conditions and parameter settings.
- Network dynamic complexity did not directly correlate with the calculated Φ value.
- The quantity of integrated information (Φ) increased with the IAF neurons' time constant, indicating a link to integrative capacity.
- The IIT measure of integrated information was found to be not resilient to internal random fluctuations (noise).
Conclusions:
- The study provides empirical evidence for the potential existence of integrated information (Φ) in simulated neurobiological systems (IAF networks).
- Neuron integrative capacity (time constant) is a significant factor influencing the level of integrated information.
- Current measures of integrated information within IIT are sensitive to noise, highlighting a limitation for real-world neural applications.
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