This course provides a tour of foundational topics in learning from a theoretical perspective. It covers a diversity of learning processes, aiming for breadth over depth (although it inevitably neglects several important forms of learning). Each meeting will consist of student-led presentations of two papers. Experience with computational modeling is not required, but students should have some familiarity with basic math (algebra and probability).

Research in science is driven by frameworks and hypotheses that determine the design and interpretation of experiments and how the field evolves. A critical discussion of these hypotheses can: raise awareness of the current state of the field, gain familiarity with terminology and concepts, sharpen critical thinking skills, and develop intuition to design effective experiments to tackle key open questions.

Course Description: Lecture and workshop series on introductory topics related to Artificial Intelligence. Each unit in the series consists of lectures on the topic and then workshops focused on building the systems covered in the lecture(s). Topics include neural networks, reinforcement learning, [neuro]evolutionary computation, and building machines that learn and think like people.

Explores the cognitive and neural processes that support attention, vision, language, motor control, navigation, and memory. Introduces basic neuroanatomy, functional imaging techniques, and behavioral measures of cognition. Discusses methods by which inferences about the brain bases of cognition are made. Considers evidence from human and animal models. Students prepare presentations summarizing journal articles.

Follows trends in modern brain theory, focusing on local neuronal circuits and deep architectures. Explores the relation between network structure, dynamics, and function. Introduces tools from information theory, dynamical systems, statistics, and learning theory in the study of experience-dependent neural computation. Specific topics include: computational principles of early sensory systems; unsupervised, supervised and reinforcement learning; attractor computation and memory in recurrent cortical circuits; noise, chaos, and coding in neuronal systems; learning and computation in deep networks in the brain and in AI systems. Cross-listed in Physics and SEAS.

Covers design and interpretation of fMRI experiments, and the relationship between fMRI and other techniques. Focuses on localization of cognitive function in the human brain. Students write papers and give presentations, explain and critique published papers, and design but do not conduct their own fMRI experiments. Upon completion, students should be able to understand and critique published fMRI papers and have a good grasp of what is known about localization of cognitive function from fMRI. Instruction and practice in written and oral communication provided. Limited to 12.

How does a human brain change over the first year of life? How do these changes support and underlie the accompanying cognitive changes in an infant's mind? This course will consider current cutting edge research addressing these topics. We will focus mostly on research in human infants, but also draw from knowledge of brain development in other model systems. Key questions include: when and how does regional function emerge in cortex? what are the effects of biological maturation versus experience of the environment in driving functional development? when and why are infant brains more plastic in the face of stroke?

Neuroscience has made huge advances in the last few years. We now know more about how the brain works than we have ever known before. Likewise, Computer Science and Artificial Intelligence have made enormous steps forward and have become part of our every-day lives. The interaction between Neuroscience and Computer Science has driven some of the most recent advances in Artificial Intelligence and this interaction has become a critical stepping stone for AI research. We have assembled a stellar list of speakers at the intersection of Neuroscience and AI from both sides of Vassar Street who will give an account of how this multi-disciplinary interaction affects their work.

This seminar, organized in coordination with the Center for Brains, Minds and Machines, will focus on the development of knowledge in the first five years. Drawing on behavioral research on infants and young children, as well as research in cognitive neuroscience, research using controlled rearing methods with animal models, and research developing and testing computational models, we consider both the starting points for human cognitive development and the ways in which early knowledge grows.

Provides instruction and dialogue on practical ethical issues relating to the responsible conduct of human and animal research in the brain and cognitive sciences. Specific emphasis on topics relevant to young researchers including data handling, animal and human subjects, misconduct, mentoring, intellectual property, and publication. Preliminary assigned readings and initial faculty lecture followed by discussion groups of four to five students each. A short written summary of the discussions submitted at the end of each class.