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

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.
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.
The Human Brain
Surveys the core perceptual and cognitive abilities of the human mind and asks how these abilities are implemented in the brain. Key themes include the representations, development, connectivity, interspecies homologies, and degree of functional specificity of particular brain regions. Also emphasizes the methods available in human cognitive neuroscience, and what inferences can and cannot be drawn from each.
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.
Audition: Neural Mechanisms, Perception and Cognition
Daniel Polley, Bertrand Delgutte, M. C. Brown
(Same subject as HST.723[J]) Neural structures and mechanisms mediating the detection, localization and recognition of sounds. General principles are conveyed by theme discussions of auditory masking, sound localization, musical pitch, cochlear implants, cortical plasticity and auditory scene analysis.
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.

Johns Hopkins University

Vision as Bayesian Inference
This is an advanced course on computer vision from a probabilistic and machine learning perspective. It covers techniques such as linear and non-linear filtering, geometry, energy function methods, markov random fields, conditional random fields, graphical models, probabilistic grammars, and deep neural networks. These are illustrated on a set of vision problems ranging from image segmentation, semantic segmentation, depth estimation, object recognition, object parsing, scene parsing, action recognition, and text captioning.
IAP 2018

Massachusetts Institute of Technology (MIT)

Photo of microscope
Course will be held the third week of IAP (week of January 22nd), M-F, from 2-5pm, in MIT room #46-3015 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. See IAP Guide for registration information. 

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.