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 2016

Massachusetts Institute of Technology (MIT)

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.
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.
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.

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.
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.

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.
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)

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.
 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.