3 papers
q-bio.NC2026
Identifying Connectivity Distributions from Neural Dynamics Using Flows
Timothy Doyeon Kim, Ulises Pereira-Obilinovic, Yiliu Wang +2
Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical dyn…
cs.LG2026
Center-Manifold Reduction of Learning at Bifurcations: Interference and Rich Learning in Recurrent Neural Networks
James Hazelden, Eric Shea-Brown
Rich learning in recurrent neural networks often proceeds through sudden transitions in latent dynamics, but there is little theory predicting how gradient descent behaves during t…
cs.LG2026
The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning
James Hazelden, Laura Driscoll, Eli Shlizerman +1
In training a neural network with gradient descent (GD), each iteration induces a linear operator that governs first-order updates to a model's internal state variables. We define…