Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks
bioRxiv Preprint Server May 15, 2024 Dana Mastrovito, Yuhan Helena Liu, Lukasz Kusmierz et al. preprint
The critical coupling strength that separates chaotic from ordered dynamics in recurrent neural networks also differentiates two learning strategies: networks initialized with low coupling learn rich representations, while those with larger variance learn lazier solutions. Training moves both stable and chaotic networks closer to the edge of chaos. Biologically realistic connectivity fosters stability across a wide range of variances. The transition to chaos is reflected in the perturbational complexity index (PCIst), a measure that clinically discriminates levels of consciousness. Networks with high PCIst exhibit stable dynamics and rich learning, suggesting a consciousness prior may promote rich learning. The results indicate a relationship between critical dynamics, learning regimes, and complexity-based measures of consciousness.