Generalized and Invariant Single-Neuron In-Vivo Activity Representation Learning

In neuroscience, models that learn representations of single-neuron in-vivo activity are essential for understanding the functional identities of individual neurons. The primary goal of these models--spanning Transformer-based, contrastive, and variational autoencoder frameworks, is not to predict neural activity, but to distill it into a stable, low-dimensional embedding that captures a neuron's intrinsic features. These learned identity embeddings should be invariant to changing experimental conditions while reflecting the neuron's molecular type and anatomical location, thus enabling downstream tasks like in-vivo cell type prediction. However, current models suffer from limited generalizability due to batch effects: non-biological variations arising from differences in experimental design, animal subjects, or recording platforms. These batch effects cause overfitting, reducing model robustness and utility.