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 2017

Harvard University

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.

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.
Spring 2017

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.
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.
Cognitive Science
Edward Gibson, Pawan Sinha
This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.
sound waves
Studies how the senses work and how physical stimuli are transformed into signals in the nervous system. Examines how the brain uses those signals to make inferences about the world, and uses illusions and demonstrations to gain insight into those inferences. Emphasizes audition and vision, with some discussion of touch, taste, and smell. Provides experience with psychophysical methods.

Harvard University

This course examines recent work applying computational models to mental disorders. These models formalize psychopathology in terms of breakdown in fundamental neurocognitive processes, linking normal and abnormal brain function within a common framework. Computational modeling has already begun to yield insights, and even possible treatments, for a wide range of disorders, including schizophrenia, autism, Parkinson’s, depression, obsessive-compulsive disorder, and attention-deficit hyperactivity disorder. The course will consist of weekly readings from the primary literature, with one student leading the discussion of each paper
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

Harvard University

Computational Cognitive Neuroscience
"What I cannot create, I do not understand." – Richard Feynman This course applies Richard Feynman's dictum to the brain, by teaching students how to simulate brain function with computer programs. Special emphasis will be placed on how neurobiological mechanisms give rise to cognitive processes like learning, memory, decision-making, and object perception. Students will learn how to understand experimental data through the lens of computational models, and ultimately how to build their own models.

Massachusetts Institute of Technology (MIT)

Neurotechnology in Action
Dr. Maxine Jonas, Prof. Alan Jasanoff
Offers a fast-paced introduction to numerous laboratory methods at the forefront of modern neurobiology. The course comprises a sequence of modules focusing on neurotechnologies being developed and used by MIT research groups. Each module consists of a background lecture and a session of firsthand and often hands-on laboratory experience. This year’s topics include multi-photon microscopy, optogenetics, expansion microscopy, high throughput neuroscience, neuromaterials and magnetic brain stimulation, high-density electrophysiology, methods in primate neuroscience, viral engineering, whole-brain optical imaging, structural, functional, and molecular magnetic resonance imaging, and magnetic encephalography.