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

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.

Massachusetts Institute of Technology (MIT)

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.
Computational Cognitive Science
Introduction to computational theories of human cognition. Focuses on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks include Bayesian and hierarchical Bayesian models, probabilistic graphical models, nonparametric statistical models and the Bayesian Occam's razor, sampling algorithms for approximate learning and inference, and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference.
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.

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).
Visual Object Recognition: Computational and Biological Mechanisms
Visual recognition is essential for most everyday tasks including navigation, reading and socialization, and is also important for engineering applications such as automatic analysis of clinical images, face recognition by computers, security tasks and automatic navigation. In spite of the enormous increase in computational power over the last decade, humans still outperform the most sophisticated engineering algorithms in visual recognition tasks. This course examines how circuits of neurons in visual cortex represent and transform visual information, covering the following topics: functional architecture of visual cortex, lesion studies, physiological experiments in humans and animals, visual consciousness, computational models of visual object recognition, computer vision algorithms.