Computational consciousness: building a self-preserving organism.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Information Theory

Background:

• Consciousness is a complex topic of significant interest in neuroscience.
• Modeling consciousness is challenging due to abstract and poorly defined brain properties.
• Recent work utilizes information theory (IT) to understand cortical information coding, emphasizing redundancy reduction.

Purpose of the Study:

• To propose a mathematical framework for understanding consciousness using information theory (IT).
• To introduce a computational approach to model consciousness.
• To explore the role of information extraction and internal states in survival and perception.

Main Methods:

• Applying information theory (IT) principles to consciousness.
• Developing a computational model termed 'organism'.
• Simulating a single-neuron organism adapting to environmental dynamics and internal states.

Main Results:

• Demonstrates a novel information-theoretic approach to consciousness.
• Illustrates a basic 'organism' model extracting maximal environmental information.
• Highlights the connection between internal states, perception, and survival strategies.

Conclusions:

• Information theory provides a viable mathematical framework for computational models of consciousness.
• An organism's survival strategy can be framed as maximizing information extraction from its environment.
• Internal states, linked to perception, are crucial for adaptation and survival in this model.