Cole Lincoln Hurwitz

We present NuCLR, a self-supervised framework that learns high-quality, population-aware neuron-level embeddings directly from spike train data using a spatio-temporal transformer and tailored contrastive loss.

We designed and tested an autoencoder model for stitching together multi-area multi-animal neuronal recording datasets and inpainting neural dynamics from unobserved brain areas in each experiment.