Perspectives on Contraction Theory and Neural Networks

Basic questions in dynamical neuroscience and machine learning motivate the study of the stability, robustness, entrainment, and computational efficiency properties of neural network models. I will present elements of an emerging contraction theory for recurrent neural networks. I will review recent advances in analyzing and training a class of robust implicit models.