NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings
Read the original on arXiv Machine Learning →NeuroLens is a self‑supervised model that learns denoised, semantically informative latent embeddings from chronic neural recordings using a Joint‑Embedding Predictive Architecture. It maps changing neural populations into a common latent space and predicts future latent states, thereby reducing sensitivity to transient recording variability. Across chronic intracortical data in mice and humans, NeuroLens improves decoding of decision‑making and semantic task variables, supports multi‑day pretraining, rapid few‑shot adaptation, and yields more stable decoding over time than existing baselines.
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