Phase Transitions in Recurrent Neural Networks (RNN): A Statistical Physics Perspective

Abstract:

This article explores the intriguing parallels between recurrent neural networks (RNNs) and spin systems in statistical physics. By modeling neurons as binary spins, we uncover how RNNs can undergo phase transitions akin to those observed in physical systems, such as the transition from liquid to gas. Utilizing the Metropolis algorithm, we simulate the energy landscape of RNNs and observe changes in magnetization over time, revealing a reduction in stable states—a hallmark of phase transitions. This interdisciplinary approach offers a novel lens for understanding the dynamics of deep learning models and suggests potential pathways for designing more stable and interpretable neural networks.