Josue Ortega Caro

This paper introduces SSLD, a framework that learns shared neural-behavioral latent dynamics and private modality-specific variables, showing it accurately reconstructs signals and isolates behaviorally relevant information across multiple datasets.

The paper uses a transformer model to analyze cholinergic and calcium signaling in mice during learning, finding spatiotemporal plasticity in frontal cortical subnetworks.

A masked-prediction model pretrained on 6,700 hours of fMRI that fine-tunes to predict clinical variables, forecasts future brain states, and recovers functional networks without supervision.

Cell2Sentence converts single-cell gene expression profiles into text-like cell sentences so standard large language models like GPT-2 can be fine-tuned for cell generation and cell-type annotation.

This study shows that robust deep learning models for object recognition rely on low spatial frequency information, as evidenced by their performance on adversarial attacks and common corruptions.