Understanding how neural activity represents higher-order cognition and how these representations evolve over time has long been a central pursuit in neuroscience.
However, current analytical tools cannot easily distinguish representational plasticity from recording instability in chronic neural recordings.
Here, we introduce NeuroLens (Latent Embeddings of Neural Semantics), a self-supervised model based on the Joint-Embedding Predictive Architecture (JEPA) framework that learns denoised, semantically informative latents from chronic neural recordings.
An adaptive encoder maps changing neural populations into a common latent space, while a temporal predictor learns structure that supports prediction of future latent states.
By predicting in latent space, NeuroLens captures temporally predictable structure and reduces sensitivity to transient, recording-specific variability.
Across chronic intracortical data in mice and humans, the learned representations improve decoding of decision-making and semantic task variables.
Multi-day pretraining enables generalization to future sessions, rapid few-shot adaptation to unseen neural populations, and more stable decoding over time than state-of-the-art baselines.
Together, these results establish NeuroLens as a new paradigm for studying how neural representations change during learning and over long timescales.