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[2503.15130] A Foundational Theory for Decentralized Sensory Learning
Summary
The article presents a foundational theory for decentralized sensory learning, proposing that biological learning mechanisms can be understood through negative feedback control systems, applicable from unicellular organisms to humans.
Why It Matters
This research offers a new perspective on sensory learning, challenging traditional global learning algorithms in neuroscience and AI. By linking evolutionary biology to learning processes, it could influence future AI development and enhance our understanding of biological systems.
Key Takeaways
- Proposes a decentralized approach to sensory learning based on negative feedback.
- Links evolutionary biology to modern learning theories in neuroscience and AI.
- Suggests that early unicellular organisms utilized similar learning principles.
- Highlights the potential for local learning algorithms without global error metrics.
- Implications for understanding the evolution of nervous systems and their functions.
Quantitative Biology > Neurons and Cognition arXiv:2503.15130 (q-bio) [Submitted on 19 Mar 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:A Foundational Theory for Decentralized Sensory Learning Authors:Linus Mårtensson, Jonas M.D. Enander, Udaya B. Rongala, Henrik Jörntell View a PDF of the paper titled A Foundational Theory for Decentralized Sensory Learning, by Linus M{\aa}rtensson and 2 other authors View PDF HTML (experimental) Abstract:In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to allow the brain to be interpreted as a negative feedback control system, and show how this could lead to local learning algorithms without the need for global error correction metrics. Thereby, a sufficiently good minima in sensory activity can be the complete reward signal of the network, as well as being both necessary and sufficient for biological learning to arise. We show that this method of learning was likely already present in the earliest unicellular life forms on earth. We show evidence that the same principle holds and scales to multicellular organisms where it in addition can lead to division of labour between cells. Available evidence shows that the evolut...
