Instructor: Justin Gardner

Course Description: How does our brain give rise to our abilities to perceive, act and think? Survey of the basic facts, empirical evidence, theories and methods of study in cognitive neuroscience exploring how cognition is instantiated in neural activity. Representative topics include perceptual and motor processes, decision making, learning and memory, attention, reward processing, reinforcement learning, sensory inference and cognitive control.

Pre-requisite: None

Course syllabus: Available on Canvas

See this article in the Stanford Daily about the course!

Instructor: Justin Gardner

Course Description: Can we know what someone is thinking by examining their brain activity? Using knowledge of the human visual system and techniques from machine learning, recent work has shown impressive ability to decode what people are looking at from their brain activity as measured with functional imaging. The course will use a combination of lectures, primary literature readings, discussion and hands-on tutorials to understand this emerging technology from basic knowledge of the perceptual (primarily visual) and other cognitive systems (such as working memory) to tools and techniques used to decode brain activity.

Pre-requisite: Psych 30 Introduction to perception or Psych 50 Introduction to cognitive neuroscience or Consent of Instructor

Course syllabus: Available on Canvas

Instructors: E.J. Chichilnisky and Justin Gardner

Course Description: This course explores the current state of brain-machine interfaces: technologies that directly stimulate and/or record neural activity. Such interfaces are being used to treat nervous system disorders, including hearing, seeing, and motor dysfunction. We expect that the range of applications will expand over the next decade to other neurological conditions and to augmentation of function. The material we cover aims to explain some of the existing technology and to clarify its limitations and promise. The course organization is designed to develop new ideas and promote new collaborations for extending the reach of these technologies. The class will feature lecturers with expertise in brain-machine interfaces of various sorts or related technologies and methods, as well as directed readings and discussion about new work in the field. In the previous year lectures were given by: Brian Wandell, Daniel Palanker, Nikos Logothetis, John Oghalai, Stephen Baccus, Paul Nuyujukian, Dan Yoshor and Nick Melosh.

Instructors: Justin Gardner and Russ Poldrack

Course Description: For first-year Neurosciences graduate students; open to other graduate students as space permits with preference given to Neuroscience students. Focus is on several domains of cognitive function where cognitive neuroscience approaches have been successfully applied across many different model systems from mice to monkeys to humans: attention, decision-making, and memory.

Syllabus: Available on Canvas

Instructor: Justin Gardner

Course Description: Decision, categorization. Bayesian inference, working memory, attention, cognitive control, conscious perception and awareness. The neural basis for all of these cognitive functions have been extensively studied in the domain of vision. Why vision? Because a great deal of scientific inquiry has delineated both the behavioral and physiological aspects of basic sensory processing in vision. Because of this, cognitive neuroscience questions can be precisely formulated in the context of vision. As a result we have some of the best answers to the question of what neural mechanisms underlie cognitive functions in the domain of vision. The course will combine lectures and in-depth discussions of primary literature to develop key concepts in the neuroscience of vision and how these concepts have been built on to understand the neural basis of higher cognition. 

Syllabus: Available on Canvas