Neuroscience Data Analysis

This lecture delves into neuroscience data analysis, covering topics such as ion currents, cellular models, and computational models. It explores the challenges of reproducibility, metadata, and standardization in neuroscience research. The instructor discusses the importance of structured data, the FAIR guiding principles, and the use of ontologies. Various computational neuroscience tools and resources, such as NEURON, NEST, and BRIAN, are highlighted. The lecture also emphasizes the significance of data curation, remote access to computational resources, and the emerging trend of computational neuroscience as a service.