A Foundational Theory for Decentralized Sensory Learning (2503.15130v1)

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 evolution of the nervous system likely was an adaptation to more effectively communicate intercellular signals to support such division of labour. We therefore propose that the same learning principle that evolved already in the earliest unicellular life forms, i.e. negative feedback control of externally and internally generated sensor signals, has simply been scaled up to become a fundament of the learning we see in biological brains today. We illustrate diverse biological settings, from the earliest unicellular organisms to humans, where this operational principle appears to be a plausible interpretation of the meaning of sensor signals in biology, and how this relates to current neuroscientific theories and findings.

Overview of "A Foundational Theory for Decentralized Sensory Learning"

The paper "A Foundational Theory for Decentralized Sensory Learning" tackles the conceptual foundation of how learning processes can evolve without reliance on global error correction mechanisms, both in biological systems and AI. The authors, Linus Mårtensson and colleagues from IntuiCell AB in conjunction with Lund University, challenge the traditional frameworks in neuroscience and AI which emphasize global learning algorithms driven by external error signals. Instead, they propose a model based on local sensory signal minimization through negative feedback control systems, starting from unicellular organisms and scaling up to complex multicellular nervous systems.

Central Thesis and Conjectures

The central thesis of the paper is derived from an evolutionary interpretation which suggests that sensory minimization—framed as negative feedback control—serves as a principal adaptive mechanism across biological systems. This proposition is anchored on a set of conjectures that delineate the sensory signals as intrinsic problems each cell must resolve locally, rather than relying on a centralized error signal for learning.

1. Sensory Minimization as Core Learning Principle: The learning across biological entities is reconceptualized as sensor problem-solving through negative feedback instead of optimizing extrinsic error measures. This posits that the minimization of sensor inputs is both necessary and sufficient for biological learning.
2. Evolutionary Continuity from Unicellular Life: The authors extrapolate that such local feedback mechanisms preexisted in unicellular organisms and were adapted as species evolved into multicellular life forms. Here, cells began to specialize and effectively distribute problem signals across a network to better resolve complex environmental and internal stimuli.
3. Neuronal Function as Signal Propagation: Neurons are positioned not as information processors per se, but as vehicles for problem signal propagation, emphasizing the evolutionary shift towards the efficient management of these signals over extensive multicellular distances and varied sensor types.

Implications for Theoretical and Practical Aspects

The authors' framework challenges existing paradigms in both neuroscience and AI by dismissing conventional backpropagation or centralized feedback control in favor of decentralized, problem-minimization approaches. This could imply a shift towards developing AI systems that operate on principles akin to biological networks, leveraging localized feedback mechanisms rather than global error correction.

Speculative Future Directions

The paper hints at potential applications encompassing new AI methodologies and mental health interventions, which could benefit from decentralized learning principles. Furthermore, it invites experimental inquiries into how these conjectures might "reframe" our understanding of cognitive and physiological phenomena in biological entities.

Through an overview of cell biology, evolutionary theory, and neural network studies, this paper presents not only a theoretical cornerstone for decentralized learning mechanisms but also envisages transformative alignments in the discourse of AI and neuroscience. The authors' conjectures suggest an inherent continuity in learning processes from the simplest life forms to the complex nervous systems, presenting profound implications for biocomputation and bio-inspired AI. The framework it lays out could potentially recalibrate the fundamental approaches to studying learning systems in biological and artificial contexts.