[2603.26506] Identifying Connectivity Distributions from Neural Dynamics Using Flows

Quantitative Biology > Neurons and Cognition arXiv:2603.26506 (q-bio) [Submitted on 27 Mar 2026] Title:Identifying Connectivity Distributions from Neural Dynamics Using Flows Authors:Timothy Doyeon Kim, Ulises Pereira-Obilinovic, Yiliu Wang, Eric Shea-Brown, Uygar Sümbül View a PDF of the paper titled Identifying Connectivity Distributions from Neural Dynamics Using Flows, by Timothy Doyeon Kim and 4 other authors View PDF HTML (experimental) Abstract:Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical dynamics. Recent work uses low-rank recurrent neural networks (lrRNNs) to infer low-dimensional latent dynamics and connectivity structure from observed activity, enabling a mechanistic interpretation of the dynamics. However, standard approaches for training lrRNNs can recover spurious structures irrelevant to the underlying dynamics. We first characterize the identifiability of connectivity structures in lrRNNs and determine conditions under which a unique solution exists. Then, to find such solutions, we develop an inference framework based on maximum entropy and continuous normalizing flows (CNFs), trained via flow matching. Instead of estimating a single connectivity matrix, our method learns the maximally unbiased distribution over connection weights consistent with observed dynamics. This approach captures complex yet necessary distributions suc...