Towards Generalizing the Information Theory for Neural Communication

Area of Science:

• Neuroscience
• Information Theory
• Computational Neuroscience

Background:

• Neuroscience utilizes information theory to quantify neural communication and coding.
• Current models often apply electronic communication assumptions to neural systems, which may be inadequate.
• Debates exist on neural information representation and transmission, particularly regarding spike timing and non-discrete states.

Purpose of the Study:

• To revise fundamental concepts for information transfer in both technical and biological systems.
• To propose an adequate interpretation of information beyond Shannon's theory's typical application range.
• To explore a time-aware approach to information theory for understanding neural operations.

Main Methods:

• Review and revision of information transfer concepts in technical and biological communication.
• Analysis of experimental evidence regarding neural spikes, communication speed, and timing precision.
• Development of a generalized information theory framework.

Main Results:

• Biological systems may use Shannon's information theory beyond its intended scope.
• Neural spikes carry information in non-discrete states, with timing precision being crucial.
• Active biological communication channels introduce power bandwidth limitations.
• A time-aware approach suggests processes, not just states, are key in neural operations.

Conclusions:

• A generalized information theory can encompass both technical and biological communication.
• Classic information theory is a specific instance within this broader generalized framework.
• Understanding neural communication requires acknowledging its unique biological constraints and dynamic nature.