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Ali Korojy

Alumni

Publications

JEDI: Jointly Embedded Inference of Neural Dynamics
Anirudh Jamkhandi
Ali Korojy
Olivier Codol
Guillaume Lajoie
Matthew G. Perich
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to … (voir plus)map the mechanisms of the brain's flexibility onto the dynamics underlying neural populations. However, identifying task-specific dynamical rules from limited, noisy, and high-dimensional experimental neural recordings remains a major challenge, as experimental data often provide only partial access to brain states and dynamical mechanisms. While recurrent neural networks (RNNs) directly constrained neural data have been effective in inferring underlying dynamical mechanisms, they are typically limited to single-task domains and struggle to generalize across behavioral conditions. Here, we introduce JEDI, a hierarchical model that captures neural dynamics across tasks and contexts by learning a shared embedding space over RNN weights. This model recapitulates individual samples of neural dynamics while scaling to arbitrarily large and complex datasets, uncovering shared structure across conditions in a single, unified model. Using simulated RNN datasets, we demonstrate that JEDI accurately learns robust, generalizable, condition-specific embeddings. By reverse-engineering the weights learned by JEDI, we show that it recovers ground truth fixed point structures and unveils key features of the underlying neural dynamics in the eigenspectra. Finally, we apply JEDI to motor cortex recordings during monkey reaching to extract mechanistic insight into the neural dynamics of motor control. Our work shows that joint learning of contextual embeddings and recurrent weights provides scalable and generalizable inference of brain dynamics from recordings alone.
2026-03-10
arXiv (prépublication)
doi.org
arxiv.org
Inferring dynamical features from neural data through joint learning of latents factors and weights
Anirudh Gururaj Jamkhandi
Ali Korojy
Olivier Codol
Guillaume Lajoie
Matthew G Perich
Behavior arises from coordinated synaptic changes in recurrent neural populations. Inferring the underlying dynamics from limited, noisy, an… (voir plus)d high-dimensional neural recordings is a major challenge, as experimental data often provide 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 hierachical 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 learnt weights provide valuable insights into network computations, highlighting the potential of joint embedding–weight learning for scalable inference of brain dynamics.
2025-09-22
NeurIPS.cc/2025/Workshop/NeurReps (poster)
openreview.net
openreview.net