Lecture
This lecture discusses the intersection of neuroscience and machine learning, focusing on how the brain processes visual information. The instructor begins by addressing the fundamental question of how the brain gives rise to the mind, particularly in the context of visual perception. Historical perspectives on neuroscience are presented, highlighting early methods of studying the brain, such as phrenology. The lecture emphasizes the advancements in recording neural activity, which have provided vast amounts of data for analysis. Despite these advancements, the instructor notes the complexity of the brain and the challenges in fully understanding its functions. The goal of the research is to model natural intelligence and the underlying neural mechanisms, with potential applications in artificial intelligence and clinical settings. The lecture also covers the importance of large-scale data for developing accurate models and discusses the benefits of integrating neuroscience insights into machine learning algorithms. Key messages include the need for experimental benchmarks and the potential for artificial neural networks to mimic brain functions.