Neuroscience and ML

This lecture covers the intersection between neuroscience and machine learning, focusing on deep learning, reinforcement learning, attention mechanisms, episodic memory, working memory, and continual learning. It explores how artificial neural networks draw inspiration from the brain's architecture and algorithmic constraints, discussing topics such as dropout regularization, episodic control, and memory systems in the mammalian brain. The lecture also delves into the future of bridging machine and human-level intelligence, emphasizing intuitive understanding of the physical world and the importance of efficient learning. It concludes with insights on computational ethology using deep learning models and the performance-optimized hierarchical models predicting neural responses in the higher visual cortex.