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)

Science of Intelligence
Explores the problem of intelligence - its nature, how it is produced by the brain and how it could be replicated in machines - with an approach that integrates computational modeling, neuroscience and cognitive science. Focuses on four intellectual thrusts: how intelligence is grounded in computation, how these computations develop in childhood, how they are implemented in neural systems, and how social interaction enhances these computations. Research within these thrusts is integrated through an overarching theme of how they contribute to a computational account of how humans analyze dynamic visual imagery to understand objects and actions in the world.
thinking robot
This course is designed to tap into fundamental aspects of biological intelligence in better understanding the nature of intelligence and designing the next generation intelligent systems. Unlike the traditional human-engineered, biological systems use adaptive, reactive, and distributed computation to learn about the environments and behave accordingly. The course starts with the fundamentals of biological computations, i.e. information, nature of computation, foundations of complex systems, and the algorithmic view of life. In the second phase, students will study different forms of biological computation and intelligence. The course is designed to step through different forms of biological intelligence, starting with simple systems and eventually reaching the neural systems and how some of the primitive forms of computation are harnessed in higher level systems. This course has a multi-disciplinary nature. It is designed based on concepts from biology, computation and physics and as a result will be of interest to students with diverse backgrounds.
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.
Neuroscience of Morality
Advanced seminar that covers both classic and cutting-edge primary literature from psychology and the neuroscience of morality. Addresses questions about how the human brain decides which actions are morally right or wrong (including neural mechanisms of empathy and self-control), how such brain systems develop over childhood and differ across individuals and cultures, and how they are affected by brain diseases (such as psychopathy, autism, tumors, or addiction). Instruction and practice in written and oral communication provided. Limited to 24.
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.

Stanford University

Computation and Cognition: The Probabilistic Approach
This course introduces the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples are drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques are discussed in concert with relevant empirical phenomena.

Johns Hopkins University

Probabilistic Models of the Visual Cortex
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning, such as deep networks.

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).
Neurons by Penn State
This course introduces students to abstract models of what and how neurons compute and concrete analyses of real neurons in action. Topics include network models of sensory processing and memory, and techniques to compare these models with real experimental data. This course will emphasize students' contributions and classroom interactions. Programming projects will be a significant aspect of the course, so programming experience (Python, Matlab) is recommended. Familiarity, but not expertise, with linear algebra and differential equations will be assumed.
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.