arXiv:2505.18069v3 Announce Type: replace Abstract: Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized by homeostatic constraints. This raises an identifiability question: does observing Hebbian/anti-Hebbian structure in synaptic updates uniquely imply an underlying Hebbian computation? We identify an alternative, emergent route. We show that near stationarity, L2 weight decay generically drives the \emph{learning-signal} component of many update rules to align with a Hebbian direction, with align
The paper indicates a future publication date (2026), suggesting this is an early look into theoretical advancements in AI learning mechanisms, particularly regarding how L2 regularization influences 'Hebbian' dynamics.
Understanding the fundamental mechanisms driving learning in AI, especially the emergent properties of widely used regularization techniques, is crucial for developing more efficient, stable, and potentially brain-inspired AI systems.
This research suggests that observed 'Hebbian' structures in synaptic updates might not always imply an intentionally designed 'Hebbian' computation, but rather an emergent property of common regularization methods.
- · AI researchers
- · Machine learning framework developers
Improved theoretical understanding of neural network training dynamics and their relationship to biological learning.
This understanding could lead to the design of more robust and biologically plausible AI learning algorithms, simplifying some aspects of neuro-inspired AI design.
Potentially, more energy-efficient or hardware-optimized AI architectures if learning rules can be simplified or made more intrinsic through intelligent regularization.
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Read at arXiv cs.LG