This course is an introductory graduate course in cellular neuroscience. As such it will assume little or no background but will rapidly progress to discussions of papers from the primarily literature. The structure of the course will be about half lectures and half discussions of new and classic papers from the primary literature. These discussions will be substantially led by students in the course. Topics covered will include ion channels and excitability, synaptic transmission and plasticity, molecular understanding of brain disease and cell biology of neurons.

This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed.

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vison, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, for example, computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans, and single-neuron recording in animals.

This course provides a series of case studies on the role of stochastic models in scientific investigation. Many contemporary theories of neural system behavior are built with statistical models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities. Some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers.

In the field of statistics, models incorporating random "noise'' components are used for data analysis. In neuroscience, the models also help form a conceptual framework for understanding neural function. We examine some of the most important methods and claims coming from applied statistical thinking. This also course introduces the mathematical theories of learning and computation by neural systems. These theories use concepts from dynamical systems and concepts from statistics to relate the dynamics and function of neural networks. We apply them to sensory computation, learning and memory, and motor control. Our learning objective is for you to formalize and mathematically answer questions about neural computations.