Inferring dynamical features from neural data through joint learning of latents factors and weights

Behavior arises from coordinated synaptic changes in recurrent neural populations. Inferring the underlying dynamics from limited, noisy, and high-dimensional neural recordings is a major challenge, as experimental data often provides only partial access to brain states. While data-driven recurrent neural networks (dRNNs) have been effective for modeling such dynamics, they are typically limited to single-task domains and struggle to generalize across behavioral conditions. Here, we propose a hierarchical model that captures neural dynamics across multiple behavioral contexts by learning a shared embedding space over RNN weights. We demonstrate that our model captures diverse neural dynamics with a single, unified model using both simulated datasets of many tasks and neural recordings during monkey reaching. Using the learned task embeddings, we demonstrate accurate classification of dynamical regimes and generalization to unseen samples. Crucially, spectral analysis on the learned weights provide valuable insights into network computations, highlighting the potential of joint embedding–weight learning for scalable inference of brain dynamics.