Lecture
This lecture delves into the intricate relationship between neuroscience and machine learning, exploring the complexity of the brain and the challenges of understanding neural data. The instructor discusses the necessity of machine learning tools for neuroscience, highlighting the inference problems faced in analyzing neural data. Various techniques such as cell segmentation, brain area segmentation, and spike sorting are explained, showcasing how machine learning can tackle these complex problems. The lecture also covers the use of deep learning algorithms for pose estimation, behavior measurement, and neural activity recording, emphasizing the importance of linking behavior to neural activity. The application of transfer learning in neuroscience research is discussed, along with the role of convolutional neural networks inspired by the visual system of mammals.