Spring 2021

Massachusetts Institute of Technology (MIT)

The Human Brain
The Human Brain
Nancy Kanwisher
Surveys the core perceptual and cognitive abilities of the human mind and asks how these are implemented in the brain. Key themes include the functional organization of the cortex, as well as the representations and computations, developmental origins, and degree of functional specificity of particular cortical regions. Emphasizes the methods available in human cognitive neuroscience, and what inferences can and cannot be drawn from each.
Cognitive Science
Cognitive Science
Joshua Tenenbaum
Edward Gibson, Pawan Sinha
Intensive survey of cognitive science. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered from behavioral, computational, and neural perspectives.
sound waves
Perception
Josh McDermott
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.
biological drawing of inner ear
Audition: Neural Mechanisms, Perception and Cognition
Josh McDermott
Daniel Polley, Bertrand Delgutte, M. C. Brown
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. Follows Harvard FAS calendar.

Harvard University

Decisions Big and Small: The cognitive science of making up your mind
Tomer Ullman
Life is full of decisions, but not all decisions are made equal. Choices can be big and consequential (should I focus on my success, family, or passion), or small and everyday (going out, or staying in). This course will introduce you to the cognitive science of judging and choosing. You will learn about rational planning, the kind a perfect intelligence might carry out; Common simplifications and shortcuts that non-perfect humans use, and how these may actually be appealing approximations for any decision-making system; Regret over choices taken and not taken; Making decisions with others, Transformative decisions, the ones that change who you are as a person. As we cover these topics, we will consider how to apply the insights from the psychology of decision making to your own ordinary and extraordinary choices.
Computational Cognitive Neuroscience
Computational Cognitive Neuroscience: Building Models of the Brain
Samuel Gershman
"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.
Fall 2019

Massachusetts Institute of Technology (MIT)

Neuroscience of Morality
Neuroscience of Morality
Rebecca Saxe
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.

Harvard University

Neurons by Penn State
Introductory Computational Neuroscience
Kenneth Blum
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, short- and long-term memory, reinforcement learning, the Hodgkin-Huxley model of the action potential, and techniques to analyze real experimental data. The approach will draw upon recent advances in neuroscience and deep learning. This course will emphasize students' contributions and classroom interactions. Programming homework assignments and group final projects will be a significant aspect of the course, so programming experience (Python/Matlab will be used) will be assumed. Familiarity with linear algebra and differential equations at the level of Math or Applied Math 21b will be assumed.
Spring 2019

Johns Hopkins University

Vision as Bayesian Inference
Vision as Bayesian Inference
Alan L. Yuille
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 2019

Massachusetts Institute of Technology (MIT)

Evolution, Computation, and Learning
Evolution, Computation, and Learning
Felix Sosa
Daniel Czegel
Here, we will explore recent work in evolutionary computation and theoretical biology modeling the processes of evolution. Namely, we will focus on these broad questions: What are the processes that govern evolution or 'evolutionary learning'? How can these processes improve upon or inspire new models or theories of learning, search, and/or development? If any, what is the role of evolutionary computation or theoretical biology in investigating human cognition or developing AI? Are there any frameworks, theories, or models that we can import from these fields?
Fall 2018

Massachusetts Institute of Technology (MIT)

Science of Intelligence
Science of Intelligence
Tomaso Poggio, Shimon Ullman, Patrick Winston
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
Bio-Inspired Intelligence
Nima Dehghani
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.

Stanford University

Computation and Cognition: The Probabilistic Approach
Computation and Cognition: The Probabilistic Approach
Noah Goodman
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
Probabilistic Models of the Visual Cortex
Alan L. Yuille
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

colorful letters on a table
Developmental Proseminar
Elizabeth Spelke
Proseminar in conceptual development and language acquisition.