A Nonlinear Separation Principle: Applications to Neural Networks, Control and Learning
Researchers introduce a nonlinear separation principle guaranteeing global stability for interconnected contracting controllers and observers in RNNs. The work derives linear matrix inequality conditions for firing-rate and Hopfield networks, establishing structural relationships that expand the admissible weight space for monotone activations.
MentionsHopfield networks · RNNs · firing-rate neural networks
Read full story at arXiv cs.LG →(arxiv.org)
Modelwire summarizes — we don’t republish. The full article lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.