Explores Graph Signal Processing applied to brain networks, emphasizing the relationship between brain function and structure using methods like Graph Fourier Transform and Structural-Decoupling Index.
Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.
Explores the intersection between neuroscience and machine learning, discussing deep learning, reinforcement learning, memory systems, and the future of bridging machine and human-level intelligence.
