mit opencourseware psychology

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Psychology by Gleitman, Fridlund, and Reisberg, 5th ed, Norton.

About this Course "Psychology is the study of human behavior and human mental life."That is the first line (or a close approximation of the first line) of most Introductory Psychology texts. That line describes an immense territory that includes single cells in the brain, your memories of childhood, the bad habitsof your roommate, and the nature of dreams...for starters. There are lots of ways to move through this material and I have tried many of them over the years. This year, I decided to try to cover the material in the order suggested by the text. As you will see on the schedule, that worked for the first half of the course.

About the Text I picked the Gleitman et al. Psychology text because it has been the best written, most intelligent of the texts on the market for many years. The book has many pages. Students who discover this fact the day before the exam are usually unhappy. The text is most useful when read in small quantities over the course of the term. Failure to read the book is likely to have adverse consequences of a fairly obvious kind.

The Lectures Lectures are scheduled two days a week, each of one and a half hour duration. The lectures and the text are intended to complement one another. In some cases, I will present material that is in the text because I think that it is best understood if heard in two different ways. In other cases, I will present material that is not found in the book. Lectures also serve to connect facts from one part of the course with material in other parts. We don't take attendance in lecture, but lectures by a live person are one of the reasons for attending college. You could read the book at home and spend a lot less time. Reading the book is a oneway street. Lectures, if you are engaged and attentive, are a two way street. I can ask you questions and I will be happy if you raise your hand to add something or ask a question or whatever. There will be a handout for most lectures. The lecture handouts are not even close to being lecture notes. They generally consist of a rough outline and a few words that I don't want to write on the board. They are not a good substitute for attending lecture. You should also talk to a friend in the class so that you will be able to decipher the handout. WARNING (and this applies beyond my course): Students who miss lectures love to drop by later and ask "Did I miss anything important?" This question, phrased in this manner, tends to provoke sarcastic answers from faculty. ("No, we saw you were not there and realized that we could not discuss anything of substance today.")

The Recitations Sections Recitations sections will be scheduled after the first meeting of the course. They are NOT optional. You are expected to be there. Your recitation instructor has primary responsibility for grading your work. Moreover, there are things that we can do in small groups that we cannot do in the 200-300 person lecture. Be there.

The Highly Sophisticated Grading Algorithm Grading will be based on four written assignments, a midterm, a final and some quizzes. Here is the formula that will form the basis for your grade: 50% (Papers) + 15% (Midterm) + 25% (Final) +10% (Other stuff including section participation & quizzes).

The Writing Assignments There are four writing assignments this year. You have some freedom in scheduling when you do various parts of them but you would be well-advised to spread the work over the term in a fairly even manner. The assignments are described in a separate handout. Some Notes About Writing

• Length: The desired length of these papers will be given in pages. Yes, a laser printer can do wonderful things putting VERY BIG and very small letters on a page. We define a page to mean about 250-300 words. That's double-spacing of a standard font like 12pt Times.
• Citations: Remember, if you use someone's ideas, give them credit by means of a citation (Franklin, 1776). The basic rules are a) You are not likely to get in trouble for having too many citations and b) it should be possible to track down the source of any assertion in your paper. If the source isn't you, let us know who it is. If you use someone's exact words, "put them in quotes" (Lincoln, 1864). Use your own words: This is important. The largest class of Bad Papers is the class of cut-and -paste collages. These are papers that are made up of direct quotes or close paraphrases of your sources. Even if the citations and bibliography are flawless, this is not a good way to write. We want your own words. Use direct quotation sparingly. Read, think, and then write.
• MIT's academic honesty policy can be found at the following link: http://web.mit.edu/policies/10.0.html
• Writing Help: 9.00 has two writing tutors assigned to it to help with your papers. To request an appointment with one of the tutors, send an email at least 48 hours in advance of your desired meeting-time. Tutors can help at any stage of the writing process, but can be most helpful if you provide them with a complete rough draft. Send your draft to the tutor (as an email attachment) at least 48 hours before your scheduled meeting.
• Deadlines: Deadlines are real. The value of papers declines monotonically after the deadline. That said, I have been teaching for quite a while and I know that people will miss deadlines. A late paper is almost always worth significantly more than no paper. Moreover, if you know you are going to be late, it is always better to tell me about it in advance. We hate surprises. Your looks betray a sudden alarm at my coming. You need not be afraid, nor treat me with violent abuse. Mine is no violent purpose. I am too old. Speaking of surprises, that last bit was a quotation (without quotation marks...oops). It was inserted to see who actually reads the syllabus. If you found this line, tell me. First person to identify the source "wins".

An Oral Presentation You will be making at least one oral presentation to the class. At the start of recitation, we will pull one or two names from a hat. Those people will be expected to get up at the end of recitation and give a 2 minute presentation on some aspect of the week's reading. It would be a Very Good Idea to come to class having read enough to say something coherent.

The Exams The course will have a midterm (1 hour) and a final (3 hours) during finals week. Exams are closed-book (and slightly strange). Note: Please don't schedule your flight home until you know the dates of your exams.

Quizzes There might be a quiz in your recitation section on any given week. It will be based on the reading from the previous week. One of their functions is benignly coercive. We wish to persuade you to do some of the reading more than 24 hrs before the exam.

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Department of Brain and Cognitive Sciences

The study of mind, brain, and behavior has grown in recent years with unprecedented speed. New avenues of approach, opened by developments in the biological and computer sciences, raise the hope that human beings, having achieved considerable mastery over the world around them, may also come closer to an understanding of themselves. The goal of the Department of Brain and Cognitive Sciences is to answer fundamental questions concerning intelligent processes and brain organization. To this end, the department focuses on four themes: molecular and cellular neuroscience, systems neuroscience, cognitive science, and computation. Several members of the department's faculty are affiliated with two major research centers: the Picower Institute for Learning and Memory and the McGovern Institute for Brain Research.

Research in cellular neuroscience deals with the biology of neurons, emphasizing the special properties of these cells as encoders, transmitters, and processors of information. Departmental researchers apply techniques of contemporary molecular and cellular biology to problems of neuronal development, structure, and function, resulting in a new understanding of the underlying basic components of the nervous system and their interactions. These studies have profound clinical implications, in part by generating a framework for the treatment of neurological and psychiatric disorders. Primary areas of interest include the development and plasticity of neuronal morphology and connectivity, the cellular and molecular bases of behavior in simple neuronal circuits, neurochemistry, and cellular physiology.

In the area of systems neuroscience, departmental investigators use a number of new approaches ranging from computation through electrophysiology to biophysics. Of major interest are the visual and motor systems where the scientific goals are to understand transduction and encoding of sensory stimuli into nerve messages, organization and development of sensorimotor systems, processing of sensorimotor information, and the sensorimotor performance of organisms. Also of major interest is neuromodulatory regulation, where the scientific goal is to understand the effects of rewarding or stressful environments on brain circuits.

In computation and cognitive science, particularly strong interactions exist between the Department of Brain and Cognitive Sciences, the Computer Science and Artificial Intelligence Laboratory, and the Center for Biological and Computational Learning, providing new intellectual approaches in areas including vision and motor control, and biological and computer learning. Computational theories are developed and tested within the framework of neurophysiological, psychological, and other experimental approaches. In the study of vision and motor control, complementary experimental work includes single-cell and multiple-cell neurophysiological recording as well as functional brain imaging. In the area of learning, which is seen as central to intelligent behavior, departmental researchers are working to develop theories of vision, motor control, neural circuitry, and language within an experimental framework.

In cognitive science, human experimentation is combined with formal and computational analyses to understand complex intelligent processes such as language, reasoning, memory, and visual information processing. There are applications in the fields of education, artificial intelligence, human-machine interaction, and in the treatment of language, cognitive, and other disorders.

Subfields in cognitive science include psycholinguistics, comprising sentence and word processing, language acquisition, and aphasia; visual cognition, including reading, imagery, attention, and perception of complex patterns such as faces, objects, and scenes; spatial cognition; memory; and the nature and development of concepts. Another key field is the study of perception—developmental and processing approaches focus on human and machine vision, and how visual images are encoded, stored, and retrieved, with current topics that include motion analysis, stereopsis, perceptual organization, and perceptual similarity. Other research includes functional brain imaging in normal subjects as well as studies of neurologically impaired patients in an attempt to understand brain mechanisms underlying normal human sensation, perception, cognition, action, and affect.

Bachelor of Science in Brain and Cognitive Sciences (Course 9)

Bachelor of science in computation and cognition (course 6-9), minor in brain and cognitive sciences, undergraduate study.

Brain science and cognitive science are complementary and interactive in their research objectives. Both approaches examine perception, performance, and intervening processes in humans and animals. Central issues in the discipline include the interpretation of sensory experience; the reception, manipulation, storage, and retrieval of information within the nervous system; and the planning and execution of motor activity. Higher-level functions include the development of formal and informal reasoning skills; and the structure, acquisition, use, and internal representation of human language.

The Bachelor of Science in Brain and Cognitive Sciences prepares students to pursue advanced degrees or careers in artificial intelligence, machine learning, neuroscience, medicine, cognitive science, psychology, linguistics, philosophy, education research and technology, and human-machine interaction.

Methods of inquiry in the brain and cognitive sciences are drawn from molecular, cellular, and systems neuroscience; cognitive and perceptual psychology; computer science and artificial intelligence; linguistics; philosophy of language and mind; and mathematics. The undergraduate program is designed to provide instruction in the relevant aspects of these various disciplines. The program is administered by an Undergraduate Officer and an Undergraduate Administrator, consulting as necessary with faculty members from these disciplines who also serve as advisors to majors, helping them select a coherent set of subjects from within the requirements, including a research requirement. Members of the faculty are available to guide the research.

The Brain and Cognitive Sciences (BCS) major incorporates programming and computational skills to meet the increasing demands for those skills in both graduate school and the workforce. The major offers a tiered system of subjects with enough flexibility to allow multiple avenues through the Brain and Cognitive Sciences curriculum, meeting the divergent goals of BCS students. Individual guidance regarding career goals is available from faculty and from Career Advising and Professional Development.

The Department of Electrical Engineering and Computer Science  and the Department of Brain and Cognitive Sciences  offer a joint curriculum leading to a Bachelor of Science in Computation and Cognition that focuses on the emerging field of computational and engineering approaches to brain science, cognition, and machine intelligence. The curriculum provides flexibility to accommodate students with a wide diversity of interests in this area—from biologically inspired approaches to artificial intelligence to reverse engineering circuits in the brain. This joint program prepares students for careers that include advanced applications of artificial intelligence and machine learning, as well as further graduate study in systems and cognitive neuroscience. Students in the program are full members of both departments, with an academic advisor from the Department of Brain and Cognitive Sciences.

Information about this program is available from the Brain and Cognitive Sciences Academic Office, Room 46-2005, 617-253-7403. 

The Minor in Brain and Cognitive Sciences consists of six subjects arranged in two levels of study, intended to provide students breadth in the field as a whole and some depth in an area of specialization.

Graduate Study

The Department of Brain and Cognitive Sciences offers programs of study leading to the doctoral degree in neuroscience or cognitive science. Areas of research specialization include cellular and molecular neuroscience, systems neuroscience, computation, and cognitive science. The graduate programs are designed to prepare students to pursue careers in research, teaching, or industry.

Doctor of Philosophy in Brain and Cognitive Sciences Fields

The doctor of philosophy in brain and cognitive sciences fields (the PhD program) is normally completed in approximately six years of full-time work, including summers. Institute requirements for the PhD are given in the section on General Degree Requirements . Formal coursework for the departmental program , described below, is intended to prepare the student to pass the general examinations and do original thesis research. The written general examinations will be due in August of the second year.

All students start with first-year intensive core subjects that provide an introduction to brain and cognitive studies from the viewpoint of systems neuroscience, molecular and cellular neuroscience, cognition, and computation. Incoming graduate students are required to take at least two of these subjects but encouraged to take all within the first two years of study. Further coursework will be diversified to give each individual the appropriate background for research in his or her own area.

Coursework in cellular and molecular neuroscience emphasizes the current genetic, molecular, and cellular approaches to biological systems that are necessary to generate advances in neuroscience.

Training in systems neuroscience covers neuroanatomy, neurophysiology, and neurotransmitter chemistry, concentrating on the major sensory, motor, memory, and executive systems in the vertebrate brain. Specific ties to molecular neurobiology or computation may be emphasized, depending upon the research interests of the student.

Coursework for students in computation is intended to give both an understanding of empirical approaches to the study of the brain and animal behavior and a theoretical background for analyzing computational aspects of biological information processing.

Candidates studying cognitive science take coursework covering such topics as language processing, language acquisition, cognitive development, natural computation, neural networks, connectionist models, and visual information processing. Students also choose seminars and coursework in linguistics, philosophy, logic, mathematics, or computer science, depending on the individual student's research program.

Graduate students begin a research apprenticeship immediately upon arrival with lab rotations in the first year. To familiarize new students with the research being conducted in the department, the department hosts a series of talks in September by faculty whose labs are open for rotations. Students typically choose their first rotation by October 1. Laboratory rotations allow students to get to know several different labs;  learn concepts and techniques, and select a laboratory in which they will complete their dissertation research. Students complete three rotations during the first year; an optional fourth rotation is also available during spring or summer term but must be approved by the rotation coordinator. Students must submit a brief rotation proposal at the start of each rotation, and a brief summary upon completion of each rotation.

At the end of the first year, an advisory committee of two to four faculty members is formed. This committee monitors progress and, with membership changing as necessary, evolves into the thesis committee. Thesis research normally requires 24-48 months of full-time activity after the qualifying examinations have been passed. It is expected that the research embodied in the PhD dissertation be original and significant work, publishable in scientific journals.

Upon successful completion of all program requirements, the student will be awarded the PhD in the corresponding field of brain and cognitive sciences.

Financial Support

Financial assistance is provided to qualified applicants in the form of traineeships, research assistantships, teaching assistantships, and a limited number of fellowships, subject to availability of funds. Prospective students are encouraged to apply for individual fellowships such as those sponsored by the National Science Foundation and the National Defense Science and Engineering Graduate Fellowship Program to cover all or part of the cost of their education. The department's financial resources for non-US citizens are limited; international students are strongly encouraged to seek financial assistance for all or part of the cost of their education from non-MIT sources.

For additional information regarding teaching and research programs, contact the Academic Administrator, Department of Brain and Cognitive Sciences, Room 46-2005, 617-253-5741, or visit the department's website .

Faculty and Teaching Staff

Michale S. Fee, PhD

Glen V. (1946) and Phyllis F. Dorflinger Professor

Professor of Neuroscience

Head, Department of Brain and Cognitive Sciences

Laura E. Schulz, PhD

Professor of Cognitive Science

Associate Head, Department of Brain and Cognitive Sciences

Josh McDermott, PhD

Associate Professor of Cognitive Science

Edward H. Adelson, PhD

John and Dorothy Wilson Professor of Vision Science

Professor of Brain and Cognitive Sciences

Polina Olegovna Anikeeva, PhD

Matoula S. Salapatas Professor of Materials Science and Engineering

Professor of Materials Science and Engineering

Mark Bear, PhD

Picower Professor of Neuroscience

(On sabbatical, fall)

Edward S. Boyden III, PhD

Y. Eva Tan Professor in Neurotechnology

Professor of Media Arts and Sciences

Professor of Biological Engineering

Emery N. Brown, MD, PhD

Edward Hood Taplin Professor of Medical Engineering

Warren M. Zapol Professor of Anaesthesia, HMS

Professor of Computational Neuroscience

Member, Institute for Data, Systems, and Society

Core Faculty, Institute for Medical Engineering and Science

Robert Desimone, PhD

Doris and Don Berkey Professor

James DiCarlo, MD, PhD

Peter deFlorez Professor of Neuroscience

Guoping Feng, PhD

James W. (1963) and Patricia T. Poitras Professor

Ila Fiete, PhD

John D. E. Gabrieli, PhD

Grover Hermann Professor of Health Sciences and Technology

Professor of Cognitive Neuroscience

Edward A. Gibson, PhD

Ann M. Graybiel, PhD

Institute Professor

Susan Hockfield, PhD

President Emerita

Neville Hogan, PhD

Sun Jae Professor in Mechanical Engineering

Alan P. Jasanoff, PhD

Professor of Nuclear Science and Engineering

Nancy Kanwisher, PhD

Walter A. Rosenblith Professor

Roger Levy, PhD

J. Troy Littleton, MD, PhD

Menicon Professor in Neuroscience

Professor of Biology

Earl K. Miller, PhD

Picower Professor

Elly Nedivi, PhD

William R. (1964) and Linda R. Young Professorship

Tomaso A. Poggio, PhD

Eugene McDermott Professor in the Brain Sciences and Human Behavior

Drazen Prelec, PhD

Digital Equipment Corp. Leaders for Global Operations Professor of Management

Professor of Management Science

Professor of Economics

Alexander Rakhlin, PhD

David Rand, PhD

Erwin H. Schell Professor

Professor of Marketing

Rebecca R. Saxe, PhD

John W. Jarve (1978) Professor of Cognitive Science

Morgan Hwa-Tze Sheng, PhD

Pawan Sinha, PhD

Professor of Vision and Computational Neuroscience

(On sabbatical, spring)

Jean-Jacques E. Slotine, PhD

Professor of Mechanical Engineering

Professor of Information Sciences

Mriganka Sur, PhD

Paul E. (1965) and Lilah Newton Professor

Joshua B. Tenenbaum, PhD

Professor of Cognitive Science and Computation

Susumu Tonegawa, PhD

Li-Huei Tsai, PhD

Fan Wang, PhD

Matthew A. Wilson, PhD

Sherman Fairchild Professor

Feng Zhang, PhD

James and Patricia Poitras (1963) Professor of Neuroscience

Associate Professors

Gloria Choi, PhD

Mark Hyman Jr Career Development Associate Professor

Associate Professor of Neuroscience

Kwanghun Chung, PhD

Associate Professor of Chemical Engineering

Associate Professor of Brain and Cognitive Sciences

Evelina Fedorenko, PhD

Associate Professor of Brain and Cognitive Neurosciences

Steven Flavell, PhD

Mark Thomas Harnett, PhD

Myriam Heiman, PhD

Mehrdad Jazayeri, PhD

Assistant Professors

Nidhi Seethapathi, PhD

Assistant Professor of Brain and Cognitive Sciences

Assistant Professor of Electrical Engineering and Computer Science

Guangyu Robert Yang, PhD

Adjunct Professors

Tari Sharot, PhD

Adjunct Professor of Brain and Cognitive Sciences

Senior Lecturers

Thomas Byrne, PhD

Senior Lecturer in Brain and Cognitive Sciences

Laura Frawley, PhD

Aida Khan, PhD

Lecturer in Brain and Cognitive Sciences

Research Staff

Principal research scientists.

Vikash Kumar Mansinghka, PhD

Principal Research Scientist of Brain and Cognitive Sciences

Ruth Rosenholtz, PhD

Research Scientists

Andrew Bolton, PhD

Research Scientist of Brain and Cognitive Sciences

Christopher Cueva, PhD

Cameron Freer, PhD

Michal Fux, PhD

Sharon Gilad-Gutnick, PhD

Melissa Kline Struhl, PhD

Laureline Logiaco, PhD

Max Siegel, PhD

Kevin A. Smith, PhD

Professors Emeriti

Emilio Bizzi, MD, PhD

Institute Professor Emeritus

Professor Emeritus of Brain and Cognitive Sciences

Martha Constantine-Paton, PhD

Professor Emerita of Neuroscience

Professor Emerita of Biology

Alan V. Hein, PhD

Professor Emeritus of Experimental Psychology

Mary C. Potter, PhD

Professor Emerita of Psychology

William G. Quinn, PhD

Professor Emeritus of Neurobiology

Professor Emeritus of Biology

Peter H. Schiller, PhD

Dorothy W. Poitras Professor Emeritus

Professor Emeritus of Medical Physiology

Gerald Edward Schneider, PhD

Professor Emeritus of Neuroscience

Kenneth Wexler, PhD

Professor Emeritus of Psychology

Professor Emeritus of Linguistics

9.00 Introduction to Psychological Science

Prereq: None U (Spring) 4-0-8 units. HASS-S

A survey of the scientific study of human nature, including how the mind works, and how the brain supports the mind. Topics include the mental and neural bases of perception, emotion, learning, memory, cognition, child development, personality, psychopathology, and social interaction. Consideration of how such knowledge relates to debates about nature and nurture, free will, consciousness, human differences, self, and society.

J. D. Gabrieli

9.000 Constructive Criticism of Research in the Brain & Cognitive Sciences

Prereq: Permission of instructor G (Fall) 3-0-3 units Can be repeated for credit.

Provides training in the constructive analysis, critique, and defense of the content of scientific papers in the brain sciences. Instruction provided in analyzing, presenting, constructively reviewing, and defending the scientific claims of cutting-edge primary research from all areas of brain sciences: molecular, systems, cognitive, and computation. Training provided by example from the instructor and practice reviewing, critiquing, presenting, and defending. Practice with instructor feedback provided to each student through constructively critiquing research and presenting/defending research. Beyond preparing for the weekly class discussion, students are also expected to attend the Brain and Cognitive Sciences colloquium each week to practice critical analysis and constructive questioning. Open to first-year graduate students in Course 9.

9.01 Introduction to Neuroscience

Prereq: None U (Fall) 4-0-8 units. REST

Introduction to the mammalian nervous system, with emphasis on the structure and function of the human brain. Topics include the function of nerve cells, sensory systems, control of movement, learning and memory, and diseases of the brain.

9.011 Systems Neuroscience Core I

Prereq: Permission of instructor G (Fall) 6-0-12 units

Survey of brain and behavioral studies. Examines principles underlying the structure and function of the nervous system, with a focus on systems approaches. Topics include development of the nervous system and its connections, sensory systems of the brain, the motor system, higher cortical functions, and behavioral and cellular analyses of learning and memory. Preference to first-year graduate students in BCS.

R. Desimone, E. K. Miller

9.012 Cognitive Science

Prereq: Permission of instructor G (Spring) 6-0-12 units

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.

E. Gibson, P. Sinha, J. Tenenbaum

9.013[J] Molecular and Cellular Neuroscience Core II

Same subject as 7.68[J] Prereq: Permission of instructor G (Spring) 3-0-9 units

Survey and primary literature review of major areas in molecular and cellular neurobiology. Covers genetic neurotrophin signaling, adult neurogenesis, G-protein coupled receptor signaling, glia function, epigenetics, neuronal and homeostatic plasticity, neuromodulators of circuit function, and neurological/psychiatric disease mechanisms. Includes lectures and exams, and involves presentation and discussion of primary literature. 9.015[J] recommended, though the core subjects can be taken in any sequence.

G. Feng, L.-H. Tsai

9.014 Quantitative Methods and Computational Models in Neurosciences

Prereq: None G (Fall) 3-1-8 units

Provides theoretical background and practical skills needed to analyze and model neurobiological observations at the molecular, systems and cognitive levels. Develops an intuitive understanding of mathematical tools and computational techniques which students apply to analyze, visualize and model research data using MATLAB programming. Topics include linear systems and operations, dimensionality reduction (e.g., PCA), Bayesian approaches, descriptive and generative models, classification and clustering, and dynamical systems. Limited to 18; priority to current BCS Graduate students.

M. Jazayeri, D. Zysman

9.015[J] Molecular and Cellular Neuroscience Core I

Same subject as 7.65[J] Prereq: None G (Fall) 3-0-9 units

Survey and primary literature review of selected major topic areas in molecular and cellular neurobiology. Covers nervous system development, axonal pathfinding, synapse formation and function, synaptic plasticity, ion channels and receptors, cellular neurophysiology, glial cells, sensory transduction, and relevant examples in human disease. Includes lectures and weekly paper write-ups, together with student presentations and discussion of primary literature. A final two-page research write-up is also due at the end of the term.

J. T. Littleton, M. Sheng

9.016[J] Introduction to Sound, Speech, and Hearing

Same subject as HST.714[J] Prereq: ( 6.3000 and 8.03 ) or permission of instructor G (Fall) Not offered regularly; consult department 4-0-8 units

See description under subject HST.714[J] .

S. S. Ghosh, H. H. Nakajima, S. Puria

9.017 Systems Neuroscience Core II

Prereq: 18.06 or ( 9.011 and 9.014 ) G (Spring) 2-2-8 units

Covers systems and computational neuroscience topics relevant to understanding how animal brains solve a wide range of cognitive tasks. Focuses on experimental approaches in systems neuroscience (behavioral design, parametric stimulus control, recording techniques) and theory-driven analyses (dynamical systems, control theory, Bayesian theory), both at the level of behavioral and neural data. Also focuses on regional organization (cortex, thalamus, basal ganglia, midbrain, and cerebellum), along with traditional divisions in systems neuroscience: sensory systems, motor systems, and associative systems.

9.021[J] Cellular Neurophysiology and Computing

Same subject as 2.794[J] , 6.4812[J] , 20.470[J] , HST.541[J] Subject meets with 2.791[J] , 6.4810[J] , 9.21[J] , 20.370[J] Prereq: ( Physics II (GIR) , 18.03 , and ( 2.005 , 6.2000 , 6.3000 , 10.301 , or 20.110[J] )) or permission of instructor G (Spring) 5-2-5 units

See description under subject 6.4812[J] .

J. Han, T. Heldt

9.07 Statistics for Brain and Cognitive Science

Prereq: 6.100B U (Fall) 4-0-8 units

Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.

E. N. Brown

9.073[J] Statistics for Neuroscience Research

Same subject as HST.460[J] Prereq: Permission of instructor G (Spring) 3-0-9 units

A survey of statistical methods for neuroscience research. Core topics include introductions to the theory of point processes, the generalized linear model, Monte Carlo methods, Bayesian methods, multivariate methods, time-series analysis, spectral analysis and state-space modeling. Emphasis on developing a firm conceptual understanding of the statistical paradigm and statistical methods primarily through analyses of actual experimental data.

9.09[J] Cellular and Molecular Neurobiology

Same subject as 7.29[J] Prereq: 7.05 or 9.01 U (Spring) 4-0-8 units

See description under subject 7.29[J] .

T. Littleton, S. Prescott

9.110[J] Nonlinear Control

Same subject as 2.152[J] Prereq: 2.151 , 6.7100[J] , 16.31 , or permission of instructor G (Spring) 3-0-9 units

See description under subject 2.152[J] .

J.-J. E. Slotine

9.12 Experimental Molecular Neurobiology

Prereq: Biology (GIR) and 9.01 U (Spring) 2-4-6 units. Institute LAB

Experimental techniques in cellular and molecular neurobiology. Designed for students without previous experience in techniques of cellular and molecular biology. Experimental approaches include DNA manipulation, molecular cloning, protein biochemistry, dissection and culture of brain cells, synaptic protein analysis, immunocytochemistry, and fluorescent microscopy. One lab session plus one paper review session per week. Instruction and practice in written communication provided. Enrollment limited.

9.123[J] Neurotechnology in Action

Same subject as 20.203[J] Prereq: Permission of instructor G (Spring) 3-6-3 units

Offers a fast-paced introduction to numerous laboratory methods at the forefront of modern neurobiology. Comprises a sequence of modules focusing on neurotechnologies that are developed and used by MIT research groups. Each module consists of a background lecture and 1-2 days of firsthand laboratory experience. Topics typically include optical imaging, optogenetics, high throughput neurobiology, MRI/fMRI, advanced electrophysiology, viral and genetic tools, and connectomics.

A. Jasanoff

9.13 The Human Brain

Prereq: 9.00 , 9.01 , or permission of instructor U (Spring) 3-0-9 units

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.

N. Kanwisher

9.17 Systems Neuroscience Laboratory

Prereq: 9.01 or permission of instructor U (Fall) 2-4-6 units. Institute LAB

Consists of a series of laboratories designed to give students experience with basic techniques for conducting systems neuroscience research. Includes sessions on anatomical, neurophysiological, and data acquisition and analysis techniques, and how these techniques are used to study nervous system function. Involves the use of experimental animals. Assignments include weekly preparation for lab sessions, two major lab reports and a series of basic computer programming tutorials (MATLAB). Instruction and practice in written communication provided.  Enrollment limited.

M. Harnett, S. Flavell

9.175[J] Robotics

Same subject as 2.165[J] Prereq: 2.151 or permission of instructor G (Fall) 3-0-9 units

See description under subject 2.165[J] .

J.-J. E. Slotine, H. Asada

9.18[J] Developmental Neurobiology

Same subject as 7.49[J] Subject meets with 7.69[J] , 9.181[J] Prereq: 7.03 , 7.05 , 9.01 , or permission of instructor U (Spring) 3-0-9 units

Considers molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Topics include: neural induction and pattern formation, cell lineage and fate determination, neuronal migration, axon guidance, synapse formation and stabilization, activity-dependent development and critical periods, development of behavior. Students taking graduate version complete additional readings that will be addressed in their mid-term and final exams.

E. Nedivi, M. Heiman

9.181[J] Developmental Neurobiology

Same subject as 7.69[J] Subject meets with 7.49[J] , 9.18[J] Prereq: 9.011 or permission of instructor G (Spring) 3-0-9 units

Considers molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Topics include: neural induction and pattern formation, cell lineage and fate determination, neuronal migration, axon guidance, synapse formation and stabilization, activity-dependent development and critical periods, development of behavior. In addition to final exam, analysis and presentation of research papers required for final grade. Students taking graduate version complete additional assignments. Students taking graduate version complete additional readings that will be addressed in their mid-term and final exams.

9.19 Computational Psycholinguistics

Subject meets with 9.190 Prereq: ( 6.100B and ( 6.3700 , 9.40 , or 24.900 )) or permission of instructor U (Fall) 4-0-8 units

Introduces computational approaches to natural language processing and acquisition by humans and machines, combining symbolic and probabilistic modeling techniques. Covers models such as n-grams, finite state automata, and context-free and mildly context-sensitive grammars, for analyzing phonology, morphology, syntax, semantics, pragmatics, and larger document structure. Applications range from accurate document classification and sentence parsing by machine to modeling human language acquisition and real-time understanding. Covers both theory and contemporary computational tools and datasets. Students taking graduate version complete additional assignments.

9.190 Computational Psycholinguistics

Subject meets with 9.19 Prereq: ( 6.100B and ( 6.3702 , 9.40 , or 24.900 )) or permission of instructor G (Fall) 4-0-8 units

9.21[J] Cellular Neurophysiology and Computing

Same subject as 2.791[J] , 6.4810[J] , 20.370[J] Subject meets with 2.794[J] , 6.4812[J] , 9.021[J] , 20.470[J] , HST.541[J] Prereq: ( Physics II (GIR) , 18.03 , and ( 2.005 , 6.2000 , 6.3000 , 10.301 , or 20.110[J] )) or permission of instructor U (Spring) 5-2-5 units

See description under subject 6.4810[J] . Preference to juniors and seniors.

9.24 Disorders and Diseases of the Nervous System

Prereq: ( 7.29[J] and 9.01 ) or permission of instructor U (Spring) 3-0-9 units

Topics examined include regional functional anatomy of the CNS; brain systems and circuits; neurodevelopmental disorders including autism; neuropsychiatric disorders such as schizophrenia; neurodegenerative diseases such as Parkinson's and Alzheimer's; autoimmune disorders such as multiple sclerosis; gliomas. Emphasis on diseases for which a molecular mechanism is understood. Diagnostic criteria, clinical and pathological findings, genetics, model systems, pathophysiology, and treatment are discussed for individual disorders and diseases. Limited to 18.

9.26[J] Principles and Applications of Genetic Engineering for Biotechnology and Neuroscience

Same subject as 20.205[J] Prereq: Biology (GIR) Acad Year 2023-2024: Not offered Acad Year 2024-2025: U (Spring) 3-0-9 units

Covers principles underlying current and future genetic engineering approaches, ranging from single cellular organisms to whole animals. Focuses on development and invention of technologies for engineering biological systems at the genomic level, and applications of engineered biological systems for medical and biotechnological needs, with particular emphasis on genetic manipulation of the nervous system. Design projects by students.

9.271[J] Pioneering Technologies for Interrogating Complex Biological Systems

Same subject as 10.562[J] , HST.562[J] Prereq: None G (Spring) 3-0-9 units

See description under subject HST.562[J] . Limited to 15.

9.272[J] Topics in Neural Signal Processing

Same subject as HST.576[J] Prereq: Permission of instructor G (Spring) 3-0-9 units

Presents signal processing and statistical methods used to study neural systems and analyze neurophysiological data. Topics include state-space modeling formulated using the Bayesian Chapman-Kolmogorov system, theory of point processes, EM algorithm, Bayesian and sequential Monte Carlo methods. Applications include dynamic analyses of neural encoding, neural spike train decoding, studies of neural receptive field plasticity, algorithms for neural prosthetic control, EEG and MEG source localization. Students should know introductory probability theory and statistics.

9.28 Current Topics in Developmental Neurobiology

Prereq: None. Coreq: 9.18[J] U (Spring) Not offered regularly; consult department 1-0-8 units

Considers recent advances in the field of developmental neurobiology based on primary research articles that address molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Also considers new techniques and methodologies as applied to the field. Students critically analyze articles and prepare concise and informative presentations based on their content. Instruction and practice in written and oral communication provided. Requires class participation, practice sessions, and presentations.

9.285[J] Audition: Neural Mechanisms, Perception and Cognition

Same subject as HST.723[J] Prereq: Permission of instructor G (Spring) 6-0-6 units

See description under subject HST.723[J] .

J. McDermott, D. Polley, B. Delgutte, M. C. Brown

9.301[J] Neural Plasticity in Learning and Memory

Same subject as 7.98[J] Prereq: Permission of instructor G (Spring) 3-0-6 units

Examination of the role of neural plasticity during learning and memory of invertebrates and mammals. Detailed critical analysis of the current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Student-directed presentations and discussions of original papers supplemented by introductory lectures. Juniors and seniors require instructor's permission.

S. Tonegawa

9.32 Genes, Circuits, and Behavior

Prereq: 7.29[J] , 9.16, 9.18[J] , or permission of instructor U (Spring) 3-0-9 units

Focuses on understanding molecular and cellular mechanisms of circuitry development, function and plasticity, and their relevance to normal and abnormal behaviors/psychiatric disorders. Highlights cutting-edge technologies for neuroscience research. Students build professional skills through presentations and critical evaluation of original research papers.

9.34[J] Biomechanics and Neural Control of Movement

Same subject as 2.183[J] Subject meets with 2.184 Prereq: 2.004 or permission of instructor G (Spring) 3-0-9 units

See description under subject 2.183[J] .

9.35 Perception

Prereq: 9.01 or permission of instructor U (Spring) 4-0-8 units

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.

J. McDermott

9.357 Current Topics in Perception

Prereq: Permission of instructor G (Spring) 2-0-7 units Can be repeated for credit.

Advanced seminar on issues of current interest in human and machine vision. Topics vary from year to year. Participants discuss current literature as well as their ongoing research.

E. H. Adelson

9.36 Neurobiology of Self

Subject meets with 9.360 Prereq: 9.01 U (Fall) 3-0-9 units

Discusses the neurobiological mechanisms that distinguish "the Self" from external environment; the neural circuits that enable us to know that "the Self" is in pain, or feels hungry, thirsty, and tired; and the neurons and circuits that lead to the emotional and moody Self. Examines brain mechanism that encodes the body schema and the Self in space. This includes the neural computations that allow, for example, the hand to know where the mouth is. Discusses the possibility of making robots develop a sense of Self, as well as disorders and delusions of the Self. Contemporary research — ranging from molecules, cells, circuits, to systems in both animal models and humans — explored. Students in the graduate version do additional classwork or projects.

9.360 Neurobiology of Self (New)

Subject meets with 9.36 Prereq: 9.01 G (Fall) 3-0-9 units

Discusses the neurobiological mechanisms that distinguish "the Self" from external environment; the neural circuits that enable us to know that "the Self" is in pain, or feels hungry, thirsty, and tired; and the neurons and circuits that lead to the emotional and moody Self. Examines brain mechanism that encodes the body schema and the Self in space. This includes the neural computations that allow, for example, the hand to know where the mouth is. Discusses the possibility of making robots develop a sense of Self, as well as disorders and delusions of the Self. Contemporary research — ranging from molecules, cells, circuits, to systems in both animal models and humans — explored. Students in the graduate version do additional classwork or projects.

9.39 Language in the Mind and Brain

Subject meets with 9.390 Prereq: 9.00 , 9.01 , or permission of instructor U (Spring) 3-0-9 units

Surveys the core mental abilities — and their neural substrates — that support language, and situates them within the broader landscape of human cognition. Topics explored include: how structured representations are extracted from language; the nature of abstract concepts and how they relate to words; the nature of the brain mechanisms that support language vs. other structured and/or meaningful inputs, like music, mathematical expressions, or pictures; the relationship between language and social cognition; how language is processed in individuals who speak multiple languages; how animal communication systems and artificial neural network language models differ from human language. Draws on evidence from diverse approaches and populations, focusing on cutting-edge research. Students taking graduate version complete additional assignments.

E. Fedorenko

9.390 Language in the Mind and Brain

Subject meets with 9.39 Prereq: 9.00 , 9.01 , or permission of instructor G (Spring) 3-0-9 units

9.40 Introduction to Neural Computation

Prereq: ( Physics II (GIR) , 6.100B , and 9.01 ) or permission of instructor U (Spring) 4-0-8 units

Introduces quantitative approaches to understanding brain and cognitive functions. Topics include mathematical description of neurons, the response of neurons to sensory stimuli, simple neuronal networks, statistical inference and decision making. Also covers foundational quantitative tools of data analysis in neuroscience: correlation, convolution, spectral analysis, principal components analysis. Mathematical concepts include simple differential equations and linear algebra.

9.401 Tools for Robust Science

Prereq: None G (Fall) 3-0-9 units

New tools are being developed to improve credibility, facilitate collaboration, accelerate scientific discovery, and expedite translation of results. Students (i) identify obstacles to conducting robust cognitive and neuroscientific research, (ii) practice using current cutting-edge tools designed to overcome these obstacles by improving scientific practices and incentives, and (iii) critically evaluate these tools' potential and limitations. Example tools investigated include shared pre-registration, experimental design, data management plans, meta-data standards, repositories, FAIR code, open-source data processing pipelines, alternatives to scientific paper formats, alternative publishing agreements, citation audits, reformulated incentives for hiring and promotion, and more. 

R. Saxe, J.DiCarlo

9.41 Research and Communication in Neuroscience and Cognitive Science

Prereq: 9.URG and permission of instructor U (Fall) 2-12-4 units

Emphasizes research and scientific communication. Instruction and practice in written and oral communication provided. Based on results of his/her UROP research, each student creates a full-length paper and a poster as part of an oral presentation at the end of the term. Other assignments include peer editing and reading/critiquing published research papers. Prior to starting class, students must have collected enough data from their UROP research projects to write a paper. Limited to juniors and seniors.

9.42 The Brain and Its Interface with the Body

Prereq: 7.28 , 7.29[J] , or permission of instructor U (Spring) Not offered regularly; consult department 3-0-9 units

Covers a range of topics, such as brain-immune system interaction, the gut-brain axis, and bioengineering approaches for studying the brain and its interactions with different organs. Explores how these interactions may be involved in nervous system disease processes.

9.422[J] Principles of Neuroengineering

Same subject as 20.452[J] , MAS.881[J] Subject meets with 20.352 Prereq: Permission of instructor G (Fall) Not offered regularly; consult department 3-0-9 units

See description under subject MAS.881[J] .

E. S. Boyden, III

9.455[J] Revolutionary Ventures: How to Invent and Deploy Transformative Technologies

Same subject as 15.128[J] , 20.454[J] , MAS.883[J] Prereq: Permission of instructor G (Fall) 2-0-7 units

See description under subject MAS.883[J] .

E. Boyden, J. Bonsen, J. Jacobson

9.46 Neuroscience of Morality

Prereq: 9.00 , 9.01 , and ( 9.13 or 9.85 ) U (Fall) Not offered regularly; consult department 5-0-7 units. HASS-S

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.

9.48[J] Philosophical Issues in Brain Science

Same subject as 24.08[J] Prereq: None Acad Year 2023-2024: Not offered Acad Year 2024-2025: U (Fall) 3-0-9 units. HASS-H; CI-H

See description under subject 24.08[J] .

E. J. Green

9.49 Neural Circuits for Cognition

Subject meets with 9.490 Prereq: 9.40 , 18.06 , or permission of instructor U (Fall) 3-0-9 units

Takes a computational approach to examine circuits in the brain that perform elemental cognitive tasks: tasks that are neither directly sensory nor directly motor in function, but are essential to bridging from perception to action. Covers circuits and circuit motifs in the brain that underlie computations like integration, decision-making, spatial navigation, inference, and other cognitive elements. Students study empirical results, build dynamical models of neural circuits, and examine the mathematical theory of representations and computation in such circuits. Considers noise, stability, plasticity, and learning rules for these systems. Students taking graduate version complete additional assignments.

9.490 Neural Circuits for Cognition

Subject meets with 9.49 Prereq: 9.40 , 18.06 , or permission of instructor G (Fall) 3-0-9 units

9.50 Research in Brain and Cognitive Sciences

Prereq: 9.00 and permission of instructor U (Fall, Spring) 0-12-0 units Can be repeated for credit.

Laboratory research in brain and cognitive science, using physiological, anatomical, pharmacological, developmental, behavioral, and computational methods. Each student carries out an experimental study under the direction of a member of the faculty. Project must be approved in advance by the faculty supervisor and the undergraduate faculty officer. Written presentation of results is required.

Consult L. Schulz

9.520[J] Statistical Learning Theory and Applications

Same subject as 6.7910[J] Prereq: 6.3700 , 6.7900 , 18.06 , or permission of instructor G (Fall) 3-0-9 units

Covers foundations and recent advances in statistical machine learning theory, with the dual goals of providing students with the theoretical knowledge to use machine learning and preparing more advanced students to contribute to progress in the field. The content is roughly divided into three parts. The first part is about classical regularization, margin, stochastic gradient methods, overparametrization, implicit regularization, and stability. The second part is about deep networks: approximation and optimization theory plus roots of generalization. The third part is about the connections between learning theory and the brain. Occasional talks by leading researchers on advanced research topics. Emphasis on current research topics.

T. Poggio, L. Rosasco

9.521[J] Mathematical Statistics: a Non-Asymptotic Approach

Same subject as 18.656[J] , IDS.160[J] Prereq: ( 6.7700[J] , 18.06 , and 18.6501 ) or permission of instructor G (Spring) 3-0-9 units

Introduces students to modern non-asymptotic statistical analysis. Topics include high-dimensional models, nonparametric regression, covariance estimation, principal component analysis, oracle inequalities, prediction and margin analysis for classification. Develops a rigorous probabilistic toolkit, including tail bounds and a basic theory of empirical processes

S. Rakhlin, P. Rigollet

9.522 Statistical Reinforcement Learning (9.651)

Prereq: None G (Fall) 9-0-3 units

Focuses on sample complexity and algorithms for online learning and decision-making. Prediction of individual sequences, online regression, and online density estimation. Multi-armed and contextual bandits. Decision-making with structured observations and the decision-estimation coefficient. Frequentist and Bayesian approaches. Reinforcement learning: tabular methods and function approximation. Behavioral and neural mechanisms of reinforcement learning.

<p class="p1">A. Rahklin

9.53 Emergent Computations Within Distributed Neural Circuits

Subject meets with 9.530 Prereq: 9.40 or permission of instructor U (Spring) 4-0-8 units

Addresses the fundamental scientific question of how the human brain still outperforms the best computer algorithms in most domains of sensory, motor and cognitive function, as well as the parallel and distributed nature of neural processing (as opposed to the serial organization of computer architectures/algorithms) required to answer it. Explores the biologically plausible computational mechanisms and principles that underlie neural computing, such as competitive and unsupervised learning rules, attractor networks, self-organizing feature maps, content-addressable memory, expansion recoding, the stability-plasticity dilemma, the role of lateral and top-down feedback in neural systems, the role of noise in neural computing. Students taking graduate version complete additional assignments.

9.530 Emergent Computations Within Distributed Neural Circuits

Subject meets with 9.53 Prereq: 9.40 or permission of instructor G (Spring) 4-0-8 units

9.55[J] Consumer Behavior

Same subject as 15.8471[J] Prereq: None U (Fall) 3-0-6 units Credit cannot also be received for 9.550[J] , 15.847[J]

See description under subject 15.8471[J] .

9.550[J] Consumer Behavior

Same subject as 15.847[J] Prereq: 15.809 , 15.814 , or permission of instructor G (Fall) 3-0-6 units Credit cannot also be received for 9.55[J] , 15.8471[J]

See description under subject 15.847[J] .

9.58 Projects in the Science of Intelligence

Prereq: ( 6.3900 and ( 9.40 or 18.06 )) or permission of instructor U (Fall) 3-0-9 units

Provides instruction on the mechanistic basis of intelligence - how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines. Examines how human intelligence emerges from computations in neural circuits to reproduce similar intelligent behavior in machines. Working in teams, students complete computational projects and exercises that reinforce the theme of collaboration between (computer science + math) and (neuroscience + cognitive science). Culminates with student presentations of their projects. Instruction and practice in oral and written communication provided. Limited to 30.

T. Poggio, S. Ullman

9.583[J] Functional Magnetic Resonance Imaging: Data Acquisition and Analysis

Same subject as HST.583[J] Prereq: 18.05 and ( 18.06 or permission of instructor) Acad Year 2023-2024: Not offered Acad Year 2024-2025: G (Fall) 2-3-7 units

See description under subject HST.583[J] .

J. Polimeni, A. Yendiki

9.59[J] Laboratory in Psycholinguistics

Same subject as 24.905[J] Prereq: None U (Spring) 3-3-6 units. Institute LAB

Hands-on experience designing, conducting, analyzing, and presenting experiments on the structure and processing of human language. Focuses on constructing, conducting, analyzing, and presenting an original and independent experimental project of publishable quality. Develops skills in reading and writing scientific research reports in cognitive science, including evaluating the methods section of a published paper, reading and understanding graphical displays and statistical claims about data, and evaluating theoretical claims based on experimental data. Instruction and practice in oral and written communication provided.

9.60 Machine-Motivated Human Vision

Prereq: None U (Spring) 2-1-9 units. Institute LAB

Explores how studies of human vision can be motivated by, and enhance the capabilities of, machine-based systems. Considers the twin questions of how the performance of state-of-the-art machine vision systems compares with that of humans, and what kinds of strategies the human visual system uses in tasks where human performance exceeds that of machines. Includes presentations by engineers from companies with significant engineering efforts in vision. Based on these presentations, students define and conduct studies to address the two aforementioned questions and present their results to the public at the end of the term. Directed towards students interested in exploring vision from computational, experimental and practical perspectives. Provides instruction and practice in written and oral communication.

9.611[J] Natural Language and the Computer Representation of Knowledge

Same subject as 6.8630[J] , 24.984[J] Prereq: 6.4100 G (Spring) 3-3-6 units

See description under subject 6.8630[J] .

R. C. Berwick

9.66[J] Computational Cognitive Science

Same subject as 6.4120[J] Subject meets with 9.660 Prereq: 6.3700 , 6.3800 , 9.40 , 18.05 , 6.3900 , or permission of instructor U (Fall) 3-0-9 units

Introduction to computational theories of human cognition. Focus on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks covered 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. Graduate students complete a final project.

J. Tenenbaum

9.660 Computational Cognitive Science

Subject meets with 6.4120[J] , 9.66[J] Prereq: Permission of instructor G (Fall) 3-0-9 units

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. Graduate students complete a final project.

9.67[J] Materials Physics of Neural Interfaces

Same subject as 3.056[J] Subject meets with 3.64[J] , 9.670[J] Prereq: 3.033 or permission of instructor Acad Year 2023-2024: Not offered Acad Year 2024-2025: U (Fall) 3-0-9 units

See description under subject 3.056[J] .

P. Anikeeva

9.670[J] Materials Physics of Neural Interfaces

Same subject as 3.64[J] Subject meets with 3.056[J] , 9.67[J] Prereq: Permission of instructor Acad Year 2023-2024: Not offered Acad Year 2024-2025: G (Fall) 3-0-9 units

See description under subject 3.64[J] .

9.72 Vision in Art and Neuroscience

Subject meets with 9.720 Prereq: None U (Fall) 2-2-8 units

Introduces and provides practical engagement with core concepts in vision neuroscience. Combination of seminar and studio work fosters interdisciplinary dialogue between visual art and vision neuroscience, culminating in a gallery exhibition of students' individual, semester-long projects. Treats the processes of visual perception and the creation of visual art in parallel, making use of the fact that both are constructive. Through lectures and readings in experimental and computational vision research, explores the hierarchy of visual processing, from the moment that light strikes the retina to the internal experience of a rich visual world. In the studio, students examine how each stage of this process manifests in the experience of art, wherein the perceptual system observes itself. Students taking graduate version complete additional assignments.

P. Sinha, S. Riskin

9.720 Vision in Art and Neuroscience

Subject meets with 9.72 Prereq: None G (Fall) 2-2-8 units

9.822[J] Psychology and Economics

Same subject as 14.137[J] Prereq: None G (Spring) 4-0-8 units

See description under subject 14.137[J] .

9.830 Graduate Student Internship (New)

Prereq: None G (Fall, Spring, Summer) Units arranged

Provides academic credit for BCS graduate students who are engaging an internship opportunity in brain or cognitive sciences. Before enrolling, students must have an offer of employment from a company or organization, and approval from their advisor and the BCS Graduate Officer.

Vallin, Sierra 

9.85 Infant and Early Childhood Cognition

Prereq: 9.00 U (Fall) 3-0-9 units. HASS-S

Introduction to cognitive development focusing on childrens' understanding of objects, agents, and causality. Develops a critical understanding of experimental design. Discusses how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. Provides instruction and practice in written communication as necessary to research in cognitive science (including critical reviews of journal papers, a literature review and an original research proposal), as well as instruction and practice in oral communication in the form of a poster presentation of a journal paper.

9.89 Off-Campus Undergraduate Research in Brain and Cognitive Sciences

Prereq: None U (Fall, IAP, Spring) Units arranged Can be repeated for credit.

For Brain and Cognitive Sciences undergraduates participating in curriculum-related research off-campus. Before enrolling, students must consult the BCS Academic Office for details on procedures and restrictions, and have approval from their faculty advisor. Subject to departmental approval. Upon completion, the off-campus supervisor will provide an evaluation of the student's work.  The student must also submit a write-up of the experience, approved by the MIT supervisor. 

9.90 Practical Experience in Brain and Cognitive Sciences

Prereq: Permission of instructor U (Summer) 0-1-0 units

For Brain and Cognitive Sciences undergraduates participating in curriculum-related off-campus professional experiences. Before enrolling, students must consult the BCS Academic Office for details on procedures and restrictions, and have approval from their faculty advisor. Subject to departmental approval. Upon completion, the student must submit a write-up of the experience, approved by the MIT supervisor.

9.900 Clinical Connection Module

Prereq: None. Coreq: 9.011 , 9.012 , 9.013[J] , 9.014 , or 9.015[J] ; permission of instructor G (Fall, Spring) Not offered regularly; consult department 0-1-0 units Can be repeated for credit.

Provides students the opportunity to connect their core neuroscience training to clinical experience (pathogenesis, diagnosis, management and therapeutic clinical trials of nervous system diseases). Students attend, along with Harvard faculty, fellows, residents and medical students at Massachusetts General Hospital, clinical seminars at MGH conducted by clinical and basic science faculty of Harvard Medical School. Each clinical experience is one week in length; students have the option to attend up to four seminars in their individual week chosen from: neuroradiology, neuropathology, neurodegenerative diseases, epilepsy, movement disorders, psychiatry, neuropsychiatric diseases and behavioral neurology, and functional neurosurgery. Seminars are followed by one-on-one discussion with instructor to connect the clinical experience with parallel course material on the neurobiology of disease.

9.901 Responsible Conduct in Science

Prereq: None G (IAP) 1-0-1 units

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.

9.91 Independent Study in Brain and Cognitive Sciences

Prereq: 9.00 , two additional subjects in Brain and Cognitive Sciences, and permission of instructor U (Fall, IAP, Spring) Units arranged Can be repeated for credit.

Individual study of a topic under the direction of a member of the faculty.

Consult Staff

9.918 BCS Grant Writing Workshop (New)

Prereq: None G (Fall) 1-0-0 units

Fellowship writing workshop to develop applications for predoctoral fellowships, including the NSF and NDSEG programs.

Kanwisher, Nancy 

9.919 Teaching Brain and Cognitive Sciences

Prereq: None G (Fall, Spring) Units arranged Can be repeated for credit.

For teaching assistants in Brain and Cognitive Sciences, in cases where teaching assignment is approved for academic credit by the department.

9.921 Research in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Fall, Spring, Summer) Units arranged Can be repeated for credit.

Guided research under the sponsorship of individual members of the faculty. Ordinarily restricted to candidates for the doctoral degree in Course 9.

9.941 Graduate Thesis Proposal

Prereq: Permission of instructor G (Fall, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

Students submit written proposals for thesis according to stated deadlines.

9.980[J] Leadership and Professional Strategies & Skills Training (LEAPS), Part I: Advancing Your Professional Strategies and Skills

Same subject as 5.961[J] , 8.396[J] , 12.396[J] , 18.896[J] Prereq: None G (Spring; second half of term) 2-0-1 units

See description under subject 8.396[J] . Limited to 80.

9.981[J] Leadership and Professional Strategies & Skills Training (LEAPS), Part II: Developing Your Leadership Competencies

Same subject as 5.962[J] , 8.397[J] , 12.397[J] , 18.897[J] Prereq: None G (Spring; first half of term) 2-0-1 units

See description under subject 8.397[J] . Limited to 80.

9.C20[J] Introduction to Computational Science and Engineering (New)

Same subject as 16.C20[J] , 18.C20[J] , CSE.C20[J] Prereq: 6.100A ; Coreq: 8.01 and 18.01 U (Fall, Spring; second half of term) 3-0-3 units Credit cannot also be received for 6.100B

See description under subject 16.C20[J] .

D. L. Darmofal, N. Seethapathi

9.S51 Special Subject in Brain and Cognitive Sciences

Prereq: 9.00 and any other two subjects in Brain and Cognitive Sciences U (Fall) Not offered regularly; consult department Units arranged Can be repeated for credit.

Undergraduate study in brain and cognitive sciences; covers material not offered in regular curriculum.

I. Pepperberg

9.S52 Special Subject in Brain and Cognitive Sciences

Prereq: 9.00 and any other two subjects in Brain and Cognitive Sciences U (Fall) Units arranged Can be repeated for credit.

9.S911 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Fall; partial term) Not offered regularly; consult department Units arranged [P/D/F] Can be repeated for credit.

Advanced graduate study in brain and cognitive sciences; covers material not offered in regular curriculum. 9.S911 is graded P/D/F.

N. G. Kanwisher

9.S912 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Fall) Units arranged Can be repeated for credit.

Advanced graduate study in brain and cognitive sciences; covers material not offered in regular curriculum.

9.S913 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Spring) Units arranged Can be repeated for credit.

M. Kellis, M. Heiman

9.S914 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Fall) Not offered regularly; consult department Units arranged Can be repeated for credit.

9.S915 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor Acad Year 2023-2024: G (Fall) Acad Year 2024-2025: Not offered Units arranged Can be repeated for credit.

9.S916 Special Subject in Brain and Cognitive Sciences

9.s917 special subject in brain and cognitive sciences.

L. Udeigwe, J. DiCarlo, R. Ajemian 

9.S918 Special Subject in Brain and Cognitive Sciences

Prereq: Permission of instructor G (Fall, Spring) Not offered regularly; consult department Units arranged [P/D/F] Can be repeated for credit.

Advanced graduate study in brain and cognitive sciences; covers material not offered in regular curriculum. 9.S918 is graded P/D/F.

9.S92 Special Subject in Brain and Cognitive Sciences

Prereq: 9.00 U (Fall) Not offered regularly; consult department Units arranged Can be repeated for credit.

Consult F. Wang

9.S93 Special Subject in Brain and Cognitive Sciences

Prereq: None U (Spring) Not offered regularly; consult department Units arranged [P/D/F]

For undergraduate study in brain and cognitive sciences; covers material not offered in regular curriculum.

9.S94 Special Subject in Brain and Cognitive Sciences

Prereq: None U (IAP) Not offered regularly; consult department Units arranged [P/D/F] Can be repeated for credit.

For undergraduate study in brain and cognitive sciences during Independent Activities Period; covers material not offered in regular curriculum. See IAP Guide for details.

9.S95 Special Subject in Brain and Cognitive Sciences

9.s96 special subject in brain and cognitive sciences, 9.s97 special subject in brain and cognitive sciences, 9.s98 special subject in brain and cognitive sciences, 9.s99 special subject in brain and cognitive sciences.

Prereq: None U (Spring) Units arranged

9.THG Graduate Thesis

Prereq: Permission of instructor G (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

Program of research leading to the writing of a Ph.D. thesis; to be arranged by the student and an appropriate MIT faculty member.

9.THM Master of Engineering Program Thesis (New)

Prereq: None G (Fall, IAP, Spring, Summer) Units arranged

Program of research leading to the writing of an MEng thesis; to be arranged by the student and an appropriate MIT faculty member. Restricted to MEng graduate students.

Vallin, Sierra

9.UR Undergraduate Research

Prereq: None U (Fall, IAP, Spring, Summer) Units arranged [P/D/F] Can be repeated for credit.

Individual participation in an ongoing research project.

9.URG Undergraduate Research

Prereq: None U (Fall, IAP, Spring, Summer) Units arranged Can be repeated for credit.

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Like millions of others during the global Covid-19 lockdowns, Emmanuel Kasigazi, an entrepreneur from Uganda, turned to YouTube to pass the time. But he wasn’t following an influencer or watching music videos. A lifelong learner, Kasigazi was scouring the video-sharing platform for educational resources. Since 2013, when he got his first smartphone, Kasigazi has been charting his own learning journey through YouTube, educating himself on subjects as diverse as psychology and artificial intelligence. And it was while searching for the answer to an AI-related question that Kasigazi first discovered MIT OpenCourseWare (OCW).

“The search results showed MIT lectures, and I thought, 'Which MIT is this?’” recalls Kasigazi, who admits he was initially skeptical as he opened the OCW YouTube channel . To his amazement, he found hundreds of courses there — not only clips, but complete lectures that he could follow alongside the students in MIT classrooms. He searched for more information on OCW and tried the channel on different browsers to triple-check its credibility. “Here they were, all these courses by one of the best — if not the best — schools in tech in the world, and they were free. For a long time I couldn’t believe it. I told everyone I knew,” he remembers.

For Kasigazi, the channel became a gateway to other open education resources, including the OpenCourseWare website and MITx courses , both part of MIT Open Learning. “I always had the questions — I grew up on science cartoons like 'Dexter’s Laboratory' and 'Pinky and the Brain' — so I would go on YouTube to try to find answers to these questions, and I found this whole other world,” he says.

OCW launched its YouTube channel in 2008, and this August passed 4 million subscribers. While introductory computer science, math, and physics are the most-visited courses on the OCW website, the most popular YouTube videos reflect a more diverse range of interests, including a lecture about piloting a fighter jet aircraft , an introduction to the human brain , and an introduction to financial terms and concepts .

Through this extensive collection, Kasigazi explains that he’s been able to explore “the things I love,” while also studying cloud computing, data science, and AI — fields that he plans to pursue in graduate studies. He says, “This is what OpenCourseWare has enabled me to do: I get the chance to not only watch the future happen, but I can actually be a part of it and create it.”  

Understanding humanity through the liberal arts

When Kasigazi was young, a beloved aunt recognized his natural curiosity and steered him toward the best schools. “I owe her everything,” he says, “everything I am is because of her.” Thanks to his excellent grades he received an academic scholarship from the Ugandan government to attend Makerere University, one of the top universities in sub-Saharan Africa, where he earned a degree in information systems. Having pursued IT for its practical applications, Kasigazi admits that he was initially more interested in the science and theory behind computers than “the coding bits of it.”

“I love the concept of it — how we are trying to make these machines,” he says, explaining that he’s long been drawn to the social sciences and humanities, particularly psychology and philosophy.

“I’m interested in how we work as human beings, because everything we do is for, with, and around human beings,” says Kasigazi, who considers psychology to be foundational to almost every field. “Whatever it is you’re teaching these kids, they’re going to be dealing with people. So first teach them what people think, how they act — that was my drive to love psychology.”

Kasigazi has also turned to OCW to brush up on his coding skills, watching 6.0001 (Introduction to Computer Science and Programming Using Python) lectures with Professor Ana Bell and reviewing the instructor-paced version with Professor Eric Grimson now on MITx . “I am proud to say MIT OCW has made me fall in love with coding … it makes sense like it never has before,” he says.

Nurturing a worldview

In 2014 Kasigazi moved to South Sudan, which had only recently emerged from a civil war as an independent nation. Fresh out of university, he was there to teach computer skills and graphic design — some of his students included members of the new country’s government — but his time in South Sudan quickly became a learning experience for him, too. “When you grow up in your community, you have this bubble. We all experience it — it’s a human thing,” he reflects. “For the first time, I realized that everything I knew is not a given. Everything I grew up knowing is not universal.”

With his worldview newly broadened, he began to nurture his interest in psychology, philosophy, and the sciences, watching crash courses, explainer videos, and other content on the subject. “It’s entertainment, to me, at the same time that it’s a passion,” he says. Today Kasigazi runs his own company, which he started in 2012 with friends and resumed when he returned to Uganda seven years ago.

Since coming across the OCW YouTube channel, Kasigazi has worked through all of the freely available MIT psychology courses. Professor John Gabrieli’s 9.00SC (Introduction to Psychology) have particularly resonated with him, even prompting him to reach out to Gabrieli. “As much as I’d been getting some knowledge on psychology over the years online, it wasn’t as deep and as interesting or captivating as your classes were,” he wrote. “From your teaching style, to the explanations, to the topics, to how you make people understand a topic, to the experiments mentioned and referenced, to how you approach questions and later make one think deeper about them.”

“The message from Emmanuel is deeply touching about the joy of learning,” says Gabrieli, who is also an investigator at the McGovern Institute. “I am so grateful to OCW for making this course on psychology open to the world, and to Emmanuel for so delightfully sharing what this course meant to him.”

New courses are added regularly to both the OCW website and YouTube channel. Kasigazi, who’s currently enjoying 9.13 (Introduction to the Human Brain) from professor and McGovern Institute investigator Nancy Kanwisher, looks forward to discovering what new worlds of knowledge they’ll open.

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Psychology OpenCourseWare: A Free Online Bachelor Level Psychology Class by MIT

'Introduction to Psychology' is an OpenCourseWare offered by the Massachusetts Institute of Technology (MIT) that explores how we think, learn, socialize and react to our environment. The free course explores human conduct from a variety of theoretical perspectives. This course is designed for undergraduate Psychology students interested in the brain and cognitive behavior.

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Introduction to psychology: course description.

This OpenCourseWare from MIT breaks down several aspects of psychology to provide an overall introduction. Human behavior, consciousness and free will are explored. Lecture notes discuss the cognitive components of psychology, such as learning and gathering information, memory and language development. Students learn about psychology as it pertains to social exchanges, romance and other encounters. Because of the personal nature of psychology, students perceive the lecture material through their own experiences and the experiences of those around them. Led by Professor Jeremy Wolfe, this free introduction to psychology course prepares students for further study.

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If you're interested in the study of psychology, visit the Introduction to Psychology course site.

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Understanding the brain. Comprehending the mind.

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The human eye can perceive about 1 million colors, but languages have far fewer words to describe those colors. The way that a language divides up color space can be influenced by contact with other languages, according to a new MIT study. Among members of the Tsimane’ society, who live in a remote part of the Bolivian Amazon rainforest, the researchers found that those who had learned Spanish as a second language began to classify colors into more words, making color distinctions that are not commonly used by Tsimane’ who are monolingual. “Learning a second language enables you to understand these concepts that you didn’t have in your first language,” says BCS professor Edward Gibson, the senior author of the study.

To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain.  Many scientists believe that to develop that intuitive understanding, the brain may use a process similar to what’s known as “self-supervised learning.” This type of machine learning, originally developed as a way to create more efficient models for computer vision, allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information. A pair of studies from researchers at the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT offers new evidence supporting this hypothesis. 

A new study from MIT and Harvard University researchers has found that adults’ understanding of conversational context and knowledge of mispronunciations that children commonly make are critical to the ability to understand children’s early linguistic efforts. Using thousands of hours of transcribed audio recordings of children and adults interacting, the research team created computational models that let them start to reverse engineer how adults interpret what small children are saying. Models based on only the actual sounds children produced in their speech did a relatively poor job predicting what adults thought children said. The most successful models made their predictions based on large swaths of preceding conversations that provided context for what the children were saying. The models also performed better when they were retrained on large datasets of adults and children interacting.