CBMM faculty offer graduate and undergraduate courses that integrate computational and empirical approaches used in the study of problems related to intelligence. These courses introduce some of the mathematical frameworks used to formulate computational models, and experimental methods used in the fields of neuroscience and cognitive science to study the neural implementations of intelligent processes and manifestation of these computations in human cognitive behavior. Examples of the integration of these perspectives are drawn from current research on intelligence. Materials for many of these courses are available online. Most graduate courses are open to advanced undergraduates with appropriate background. Enrollment for courses is handled through the respective institutions.

Fall 2018

Massachusetts Institute of Technology (MIT)

Principles of Neuroengineering
Covers how to innovate technologies for brain analysis and engineering, for accelerating the basic understanding of the brain, and leading to new therapeutic insight and inventions. Focuses on using physical, chemical and biological principles to understand technology design criteria governing ability to observe and alter brain structure and function. Topics include optogenetics, noninvasive brain imaging and stimulation, nanotechnologies, stem cells and tissue engineering, and advanced molecular and structural imaging technologies. Design projects by students.
Artificial Intelligence
Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.
Close up image of squid skin
Tutorial series in computational topics related to brain and cognitive sciences. Each tutorial will consist of a short lecture, and then 'office hours' time to work through practice problems, and discuss problems people want help with in their own research. Food will be provided.

Harvard University

Learning Theory C Map
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).
Spring 2018

Massachusetts Institute of Technology (MIT)

The Human Intelligence Enterprise
Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on learning, language, vision, event representation, commonsense reasoning, self reflection, story understanding, and analogy. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the implications of work on brain scanning, developmental psychology, and cognitive psychology. Emphasis on discussion and analysis of original papers. Students taking graduate version complete additional assignments.
Neurotechnology in Action
Dr. Maxine Jonas, Prof. Alan Jasanoff
Offers a fast-paced introduction to numerous laboratory methods at the forefront of modern neurobiology. Comprises a sequence of modules focusing on neurotechnologies that are developed and used by MIT research groups. Each module consists of a background lecture and 1-2 days of firsthand laboratory experience. Topics typically include optical imaging, optogenetics, high throughput neurobiology, MRI/fMRI, advanced electrophysiology, viral and genetic tools, and connectomics.
IAP 2018

Massachusetts Institute of Technology (MIT)

Memory Wars
Lindsey Williams
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.
Fall 2017

University of Central Florida

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.
Spring 2017

Massachusetts Institute of Technology (MIT)

Cognitive Neuroscience
Earl Miller
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.

Harvard University

Computational Neuroscience
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.
Fall 2016

Massachusetts Institute of Technology (MIT)

Functional MRI Investigations of the Human Brain
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.
Spring 2016

Massachusetts Institute of Technology (MIT)

Development of the Human Mind and Brain in the First Year
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?
IAP 2016

Massachusetts Institute of Technology (MIT)

 The Science and Engineering of Intelligence: A bridge across Vassar Street
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.
Spring 2015

Massachusetts Institute of Technology (MIT), Harvard University

Seminar in cognitive development
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.
IAP 2015

Massachusetts Institute of Technology (MIT)

Photo of microscope
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.