PERSPECTIVE article
Front. Netw. Physiol.
Sec. Networks in the Brain System
Volume 5 - 2025 | doi: 10.3389/fnetp.2025.1678473
This article is part of the Research TopicAI-Driven Models Transforming Perceptual Science: Self-Organizing Intelligence for Sensory CognitionView all articles
The Precision Principle: Driving Biological Self-Organization
Provisionally accepted
- 1Carleton University, Ottawa, Canada
- 2Universite de Geneve, Geneva, Switzerland
- 3ICube UMR 7357 CNRS and Université de Strasbourg, Centre National de la Recherche Scientifique (CNRS), Paris, France
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
In this perspective, we introduce the Precision Principle as a unifying theoretical framework to explain self-organization across biological systems. Drawing from neurobiology, systems theory, and computational modeling, we propose that precision, understood as constraint-driven coherence, is the key force shaping the architecture, function, and evolution of nervous systems. We identify three interrelated domains: Structural Precision (efficient, modular wiring), Functional Precision (adaptive, context-sensitive circuit deployment), and Evolutionary Precision (selection-guided architectural refinement). Each domain is grounded in local operations such as spatial and temporal averaging, multiplicative co-activation, and threshold gating, which enable biological systems to achieve robust organization without centralized control. Within this framework, we introduce a Precision Coefficient, 𝑃(𝑧) = 𝐶(𝑧) −𝛼𝑅(𝑧), which formalizes the balance between network coherence and resource cost and serves as a simple quantitative outline of the principle. Conceptually, this formalism aligns with established learning mechanisms: Hebbian reinforcement provides the local substrate for weight changes, while winner-take-all and k-winners competition selectively eliminates weaker synapses, together increasing 𝐶(𝑧)and reducing redundancy within 𝑅(𝑧). Rather than framing the theory in opposition to existing models, we aim to establish the Precision Principle as an original, integrative lens for understanding how systems sustain efficiency, flexibility, and resilience. We hope the framework inspires new research into neural plasticity, development, and artificial systems, by centering internal coherence, not prediction or control, as the primary driver of self-organizingintelligence
Keywords: self-organization;, Brain, evolution, neural circuits, Neural learning, Precision
Received: 08 Sep 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Roy, Sidhu, Byczynski, D'Angiulli and Dresp-Langley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Birgitta Dresp-Langley, birgitta.dresp@cnrs.fr
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.