Computational Neuroscience

Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course surveys computational modeling and data analysis methods for neuroscience. Example topics are short-term memory and decision-making, population coding, modeling behavioral and neural data, and reinforcement learning. Classes are a mix of lectures from the professor, and presentations of research papers by the students. Two 90 minute lectures, one laboratory. Lectures in common between NEU 437/NEU 537. Graduate students carry out a semester-long project.