Artificial Intelligence & Computational Neuroscience

Artificial Intelligence and Computational Neuroscience are opening new possibilities for understanding complex brain systems and neurological diseases. This track highlights machine learning, deep learning, computational modeling, neural networks, and data-driven approaches to neuroscience. Applications include neurological diagnosis, brain-image analysis, disease prediction, drug discovery, digital biomarkers, and personalized treatment. Computational models can help researchers investigate neural circuits, brain connectivity, cognition, learning, and disease mechanisms. The track welcomes research using multimodal imaging, genomic information, wearable technologies, electronic health data, and large neuroscience datasets. AI-assisted drug discovery can support identification of new CNS targets and therapeutic compounds. Researchers may also address important challenges involving data quality, model validation, interpretability, privacy, and clinical implementation. This interdisciplinary track brings together neuroscientists, neurologists, computer scientists, engineers, pharmacologists, and data researchers. The focus is on transforming complex neurological data into meaningful knowledge and developing intelligent tools that can support more accurate diagnosis, discovery, and personalized neurological care.

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