During the first two weeks, there will be lectures in the morning, and evening tutorials will consist of analytical and computational application of the concepts learned in the morning. The third week consists of lectures in the mornings and projects in the evening.

First week

The first week will consist of a mini-course in Machine Learning, especially those techniques used in Neuroscience. This will include supervised and unsupervised learning, learning for time series and Reinforcement Learning, as well as a day dedicated to practical aspects in the use of Machine Learning Techniques.

Day 1: History of AI and the relationship between Machine Learning and Neuroscience
Day 2: Supervised Learning
Day 3: Unsupervised Learning
Day 4: Practical consideration of the use of Machine Learning
Day 5: Learning for Time Series
Day 6: Reinforcement Learning

Second Week

The second week discusses how Machine Learning techniques are applied in Neuroscience. This will include the Machine Learning tools used to extract information from datasets of animal behavior and brain activity, Machine Learning models from brain activity and behavior, models using spiking neural networks and which are the limits ofML techniques.

Day 7: ML tools in animal behavior
Day 8: ML tools in brain activity
Day 9: Models derived from the brain and behavior
Day 10: ML-based models of the brain & discrepancies
Day 11: Theory-based modeling, and its connection to ML
Day 12: Bio-inspired ML/spiking networks

Third week

The third week has lectures on more advanced topics and evenings on projects.

Students will design projects applying machine learning (ML) methods to neural recording and animal behavior data sets. Neural recording data sets provided will be from a variety of techniques, including electrophysiology, calcium imaging, and fMRI. Behavior data sets provided will be from video. Students may focus on developing new ML algorithms for improving automated tracking, segmentation, categorization, or representation learning. Alternatively, they may focus on applying these ML methods to large data sets to discover new biological insights. A third option is to explore ML-based computational models of neural activity or behavior. Through these projects, students will gain hands-on experience using modern ML approaches, coding in PyTorch, and gain a deeper understanding of the power and potential failures of ML.