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The precision principle: driving biological self-organization.

Raymond Roy1,2, Kiranpreet Sidhu1,2, Gabriel Byczynski3

  • 1Department of Neuroscience, Neuroscience of Imagination, Cognition, and Emotion Research Lab, Carleton University, Ottawa, ONT, Canada.

Frontiers in Network Physiology
|November 28, 2025
PubMed
Summary

The Precision Principle explains biological self-organization through constraint-driven coherence. This framework details structural, functional, and evolutionary precision in nervous systems, driven by local operations and quantified by the Precision Coefficient.

Keywords:
brainevolutionnetworksneural circuitsneural learningplasticityprecisionself-organization

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Area of Science:

  • Neurobiology
  • Systems Theory
  • Computational Modeling

Background:

  • Biological systems exhibit complex self-organization.
  • Existing models often focus on prediction or control.
  • A unifying framework for understanding self-organization is needed.

Purpose of the Study:

  • Introduce the Precision Principle as a theoretical framework for self-organization.
  • Explain how precision drives the architecture, function, and evolution of nervous systems.
  • Provide a quantitative measure, the Precision Coefficient, to formalize the principle.

Main Methods:

  • Drawing from neurobiology, systems theory, and computational modeling.
  • Identifying three domains: Structural, Functional, and Evolutionary Precision.
  • Proposing local operations like averaging, co-activation, and gating.

Main Results:

  • Precision, defined as constraint-driven coherence, is proposed as the key organizing force.
  • Three domains illustrate precision's role in wiring, circuit deployment, and architectural refinement.
  • The Precision Coefficient (P(z) = C(z) - αR(z)) balances network coherence and resource cost.

Conclusions:

  • The Precision Principle offers an integrative lens for understanding self-organizing intelligence.
  • It highlights internal coherence, not prediction or control, as the primary driver.
  • The framework aims to inspire research in neural plasticity, development, and artificial systems.