Intro to Computational Neuroscience

4.0 credits

Computational neuroscience studies how the brain can be understood in terms of computations implemented by neural circuits, and in terms of using computational methods to analyze neural and behavioral data. This course for advanced undergraduates and graduate students starts with models of individuals neurons before moving on to networks of neurons and behavior. It provides both a classic signal processing, and a probabilistic perspective on how neurons support the brain’s computations. While primarily lecture-based, an important part of the course are exercises that typically consist on implementing (programming) a model discussed in the class and analyze its behavior. The course also provides the opportunity for a final project but this is not required. The material mostly considers the sensory system and perceptual decision-making. There are no formal prerequisites. However, programming experience and a minimal background in linear algebra (vectors and matrices) and analysis (basic ordinary differential equations) are essential. At the beginning, there will be a very brief introduction to the key biological concepts necessary for the course. In doubt, please email the instructor.