Topic: disentanglement

Deep learning models have shown significant efficacy in predicting neural activity, offering promising avenues for understanding complex neural dynamics. However, disentangling key explanatory factors---crucial for achieving generalizable and interpretable neural activity predictions---remains underexplored. Such disentanglement is essential for downstream simulations and for elucidating inter-individual variability in neural responses. To address this gap, we introduce the Query-based Neural Activity Prediction (qNAP) model, a novel framework that leverages an attention mechanism to explicitl