The students will learn the basics of AI applications which are used in neuroscience. They will learn basic techniques of how to apply AI in their example neuroscience data.
The course introduces students to concepts of AI in neuroscience in:
- Decode brain activity: AI models analyze complex data from EEG, fMRI, and other neuroimaging techniques to understand how the brain functions.
- Diagnose neurological disorders: Machine learning helps detect patterns linked to conditions like Alzheimer's, epilepsy, and Parkinson’s disease
- Enhance brain-computer interfaces (BCIs): AI enables real-time decoding of neural signals, allowing people to control devices with their thoughts
- Model biological intelligence: Neuroscience-inspired AI tries to replicate how the brain learns, adapts, and processes information.
- Cross-Pollination: Neuroscience Inspires AI (and vice versa) These applications often require interpretable, biologically plausible models—not just high accuracy, but insights into how the brain works.
Covers:
- Deep learning models (CNNs)
- Explainable AI, mechanistic interpretability
- Teacher
Harri Merisaari