CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics Fatih Dinc Department of Applied Physics Stanford University Stanford, CA94305 Adam Shai

Feb-16-2026, 05:43:47 GMT–Neural Information Processing Systems 

A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs).