JEDI: Jointly Embedded Inference of Neural Dynamics
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to 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...
| Authors | Anirudh Jamkhandi, Ali Korojy, Olivier Codol et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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