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Learning-Induced Dynamical Transition in Recurrent Neural Networks

This story is from 2026-09-18. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.19288v1 Announce Type: new Abstract: 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 tra…

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  1. 2026-09-18 04:00 · arXiv cs.LG
    Learning-Induced Dynamical Transition in Recurrent Neural Networks

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