Deep learning (DL) methods have significantly advanced the fields of neuroscience and physiology. However, conventional DL methods that are tailored to specific populations and tasks are no longer adequate in comprehending large-scale, multimodal, and multitask physiological datasets. In this thesis, we propose methods that aim to improve DL methods from the perspective of: (i) Generalizability, enabling applications across diverse modalities, tasks, and subjects, and (ii) Explainability, enabling researchers to understand and potentially customize the learning process to suit specific distributions. These improvements are not only crucial for physiological datasets, which typically require domain knowledge to comprehend, but also improve deep learning methodologies and benefit the broader ML community.