Deep Representation Learning Reveals Factorized Neural Codes in Whole-Brain Population Dynamics
Year: 2026
Type: preprint
Abstract: Extracting interpretable representations from high-dimensional whole-brain neural dynamics remains a major computational challenge in systems neuroscience. Here, we develop a wiring-agnostic deep-learning framework that combines convolutional encoding with temporal transformers to learn compact representations directly from volumetric calcium imaging of the entire Drosophila melanogaster brain, without neuronal identification or anatomical annotation. Applied to brain-wide activity recorded across 16 factorially combined sensory and internal-state conditions, the learned representations revealed a factorized organization of whole-brain dynamics: metabolic state, sensory modality, and stimulus valence emerged along three near-orthogonal axes from a classification objective based only on flat class labels and without explicit disentanglement constraints. Spatial attribution and brain region-level ablation analyses linked modality representations to anatomically distinct circuits, whereas state- and valence-related information showed a broadly distributed organization. Our approach provides a scalable framework for discovering interpretable representations of whole-brain neural dynamics.
Institutions: University of Cologne, University of Bonn
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W7160989819