Learning-Induced Dynamical Transition in Recurrent Neural Networks
Unlocking the mechanics of how chaotic neural networks evolve into structured, task-ready systems.
Learning in recurrent neural networks can fundamentally reshape their underlying dynamics, transforming initially chaotic activity into stable task-dependent behavior. We develop a non-equilibrium dynamical mean-field theory(DMFT) to describe this transition during learning. We s...
ArXiv CS.LG
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This research provides a mathematical framework for understanding how neural networks transition from chaotic states to structured, goal-oriented processing during training. By applying non-equilibrium statistical mechanics, it offers a window into the emergence of stable cognitive architectures in artificial systems.
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