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8.624 | Plasma Waves | Comprehensive theory of electromagnetic waves in a magnetized plasma. Wave propagation in cold and hot plasmas. Energy flow. Absorption by Landau and cyclotron damping and by transit time magnetic pumping (TTMP). Wave propagation in inhomogeneous plasma: accessibility, WKB theory, mode conversion, connection formulae, and Budden tunneling. Applications to RF plasma heating, wave propagation in the ionosphere and laser-plasma interactions. Wave propagation in toroidal plasmas, and applications to ion cyclotron (ICRF), electron cyclotron (ECRH), and lower hybrid (LHH) wave heating. Quasi-linear theory and applications to RF current drive in tokamaks. Extensive discussion of relevant experimental observations. | true | Spring | Graduate | 3-0-9 | 22.611 | null | false | false | false | False | False | False |
8.641 | Physics of High-Energy Plasmas I | Physics of High-Energy Plasmas I and II address basic concepts of plasmas, with temperatures of thermonuclear interest, relevant to fusion research and astrophysics. Microscopic transport processes due to interparticle collisions and collective modes (e.g., microinstabilities). Relevant macroscopic transport coefficients (electrical resistivity, thermal conductivities, particle "diffusion"). Runaway and slide-away regimes. Magnetic reconnection processes and their relevance to experimental observations. Radiation emission from inhomogeneous plasmas. Conditions for thermonuclear burning and ignition (D-T and "advanced" fusion reactions, plasmas with polarized nuclei). Role of "impurity" nuclei. "Finite-β" (pressure) regimes and ballooning modes. Convective modes in configuration and velocity space. Trapped particle regimes. Nonlinear and explosive instabilities. Interaction of positive and negative energy modes. Each subject can be taken independently. | true | Fall | Graduate | 3-0-9 | 22.611 | null | false | false | false | False | False | False |
8.642 | Physics of High-Energy Plasmas II | Physics of High-Energy Plasmas I and II address basic concepts of plasmas, with temperatures of thermonuclear interest, relevant to fusion research and astrophysics. Microscopic transport processes due to interparticle collisions and collective modes (e.g., microinstabilities). Relevant macroscopic transport coefficients (electrical resistivity, thermal conductivities, particle "diffusion"). Runaway and slide-away regimes. Magnetic reconnection processes and their relevance to experimental observations. Radiation emission from inhomogeneous plasmas. Conditions for thermonuclear burning and ignition (D-T and "advanced" fusion reactions, plasmas with polarized nuclei). Role of "impurity" nuclei. "Finite-β" (pressure) regimes and ballooning modes. Convective modes in configuration and velocity space. Trapped particle regimes. Nonlinear and explosive instabilities. Interaction of positive and negative energy modes. Each subject can be taken independently. | true | Fall | Graduate | 3-0-9 | 22.611 | null | false | false | false | False | False | False |
8.670[J] | Principles of Plasma Diagnostics | Introduction to the physical processes used to measure the properties of plasmas, especially fusion plasmas. Measurements of magnetic and electric fields, particle flux, refractive index, emission and scattering of electromagnetic waves and heavy particles; their use to deduce plasma parameters such as particle density, pressure, temperature, and velocity, and hence the plasma confinement properties. Discussion of practical examples and assessments of the accuracy and reliability of different techniques. | true | Fall | Graduate | 4-4-4 | 22.611 | 22.67[J] | false | false | false | False | False | False |
8.681, | 8.682 Selected Topics in Fluid and Plasma Physics | Presentation of topics of current interest, with content varying from year to year. Subject not routinely offered; given when interest is indicated. | true | Fall, Spring | Graduate | 3-0-9 | 22.611 | null | false | false | false | False | False | False |
8.701 | Introduction to Nuclear and Particle Physics | The phenomenology and experimental foundations of particle and nuclear physics; the fundamental forces and particles, composites. Interactions of particles with matter, and detectors. SU(2), SU(3), models of mesons and baryons. QED, weak interactions, parity violation, lepton-nucleon scattering, and structure functions. QCD, gluon field and color. W and Z fields, electro-weak unification, the CKM matrix. Nucleon-nucleon interactions, properties of nuclei, single- and collective- particle models. Electron and hadron interactions with nuclei. Relativistic heavy ion collisions, and transition to quark-gluon plasma. | true | Fall | Graduate | 3-0-9 | None. Coreq: 8.321 | null | false | false | false | False | False | False |
8.711 | Nuclear Physics | Modern, advanced study in the experimental foundations and theoretical understanding of the structure of nuclei, beginning with the two- and three-nucleon problems. Basic nuclear properties, collective and single-particle motion, giant resonances, mean field models, interacting boson model. Nuclei far from stability, nuclear astrophysics, big-bang and stellar nucleosynthesis. Electron scattering: nucleon momentum distributions, scaling, olarization observables. Parity-violating electron scattering. Neutrino physics. Current results in relativistic heavy ion physics and hadronic physics. Frontiers and future facilities. | true | Spring | Graduate | 4-0-8 | 8.321 and 8.701 | null | false | false | false | False | False | False |
8.712 | Advanced Topics in Nuclear Physics | Subject for experimentalists and theorists with rotation of the following topics: (1) Nuclear chromodynamics-- introduction to QCD, structure of nucleons, lattice QCD, phases of hadronic matter; and relativistic heavy ion collisions. (2) Medium-energy physics-- nuclear and nucleon structure and dynamics studied with medium- and high-energy probes (neutrinos, photons, electrons, nucleons, pions, and kaons). Studies of weak and strong interactions. | true | Fall, Spring | Graduate | 3-0-9 | 8.711 or permission of instructor | null | false | false | false | False | False | False |
8.751[J] | Quantum Technology and Devices | Examines the unique features of quantum theory to generate technologies with capabilities beyond any classical device. Introduces fundamental concepts in applied quantum mechanics, tools and applications of quantum technology, with a focus on quantum information processing beyond quantum computation. Includes discussion of quantum devices and experimental platforms drawn from active research in academia and industry. Students taking graduate version complete additional assignments. | true | Spring | Graduate | 3-0-9 | 22.11 | 22.51[J] | false | false | false | False | False | False |
8.781, | 8.782 Selected Topics in Nuclear Theory | Presents topics of current interest in nuclear structure and reaction theory, with content varying from year to year. Subject not routinely offered; given when sufficient interest is indicated. | true | Fall, Spring | Graduate | 3-0-9 | 8.323 | null | false | false | false | False | False | False |
8.811 | Particle Physics | Modern review of particles, interactions, and recent experiments. Experimental and analytical methods. QED, electroweak theory, and the Standard Model as tested in recent key experiments at ee and pp colliders. Mass generation, W, Z, and Higgs physics. Weak decays of mesons, including heavy flavors with QCD corrections. Mixing phenomena for K, D, B mesons and neutrinos. CP violation with results from B-factories. Future physics expectations: Higgs, SUSY, sub-structure as addressed by new experiments at the LHC collider. | true | Fall | Graduate | 3-0-9 | 8.701 | null | false | false | false | False | False | False |
8.812 | Graduate Experimental Physics | Provides practical experience in particle detection with verification by (Feynman) calculations. Students perform three experiments; at least one requires actual construction following design. Topics include Compton effect, Fermi constant in muon decay, particle identification by time-of-flight, Cerenkov light, calorimeter response, tunnel effect in radioactive decays, angular distribution of cosmic rays, scattering, gamma-gamma nuclear correlations, and modern particle localization. | true | IAP | Graduate | 1-8-3 | 8.701 | null | false | false | false | False | False | False |
8.821 | String Theory | An introduction to string theory. Basics of conformal field theory; light-cone and covariant quantization of the relativistic bosonic string; quantization and spectrum of supersymmetric 10-dimensional string theories; T-duality and D-branes; toroidal compactification and orbifolds; 11-dimensional supergravity and M-theory. Meets with 8.251 when offered concurrently. | false | Fall | Graduate | 3-0-9 | 8.324 | null | false | false | false | False | False | False |
8.831 | Supersymmetric Quantum Field Theories | Topics selected from the following: SUSY algebras and their particle representations; Weyl and Majorana spinors; Lagrangians of basic four-dimensional SUSY theories, both rigid SUSY and supergravity; supermultiplets of fields and superspace methods; renormalization properties, and the non-renormalization theorem; spontaneous breakdown of SUSY; and phenomenological SUSY theories. Some prior knowledge of Noether's theorem, derivation and use of Feynman rules, l-loop renormalization, and gauge theories is essential. | false | Fall | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.851 | Effective Field Theory | Covers the framework and tools of effective field theory, including: identifying degrees of freedom and symmetries; power counting expansions (dimensional and otherwise); field redefinitions, bottom-up and top-down effective theories; fine-tuned effective theories; matching and Wilson coefficients; reparameterization invariance; and advanced renormalization group techniques. Main examples are taken from particle and nuclear physics, including the Soft-Collinear Effective Theory. | true | Spring | Graduate | 3-0-9 | 8.324 | null | false | false | false | False | False | False |
8.871 | Selected Topics in Theoretical Particle Physics | Presents topics of current interest in theoretical particle physics, with content varying from year to year. Subject not routinely offered; given when sufficient interest is indicated. | false | Spring | Graduate | 3-0-9 | 8.323 | null | false | false | false | False | False | False |
8.872 | Selected Topics in Theoretical Particle Physics | Presents topics of current interest in theoretical particle physics, with content varying from year to year. Subject not routinely offered; given when sufficient interest is indicated. | true | Fall | Graduate | 3-0-9 | 8.323 | null | false | false | false | False | False | False |
8.881, | 8.882 Selected Topics in Experimental Particle Physics | Presents topics of current interest in experimental particle physics, with content varying from year to year. Subject not routinely offered; given when sufficient interest is indicated. | true | Fall, Spring | Graduate | 3-0-9 | 8.811 | null | false | false | false | False | False | False |
8.901 | Astrophysics I | Size and time scales. Historical astronomy. Astronomical instrumentation. Stars: spectra and classification. Stellar structure equations and survey of stellar evolution. Stellar oscillations. Degenerate and collapsed stars; radio pulsars. Interacting binary systems; accretion disks, x-ray sources. Gravitational lenses; dark matter. Interstellar medium: HII regions, supernova remnants, molecular clouds, dust; radiative transfer; Jeans' mass; star formation. High-energy astrophysics: Compton scattering, bremsstrahlung, synchrotron radiation, cosmic rays. Galactic stellar distributions and populations; Oort constants; Oort limit; and globular clusters. | true | Spring, Spring | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.902 | Astrophysics II | Galactic dynamics: potential theory, orbits, collisionless Boltzmann equation, etc. Galaxy interactions. Groups and clusters; dark matter. Intergalactic medium; x-ray clusters. Active galactic nuclei: unified models, black hole accretion, radio and optical jets, etc. Homogeneity and isotropy, redshift, galaxy distance ladder. Newtonian cosmology. Roberston-Walker models and cosmography. Early universe, primordial nucleosynthesis, recombination. Cosmic microwave background radiation. Large-scale structure, galaxy formation. | true | Fall | Graduate | 3-0-9 | 8.901 | null | false | false | false | False | False | False |
8.913 | Plasma Astrophysics I | For students interested in space physics, astrophysics, and plasma physics in general. Magnetospheres of rotating magnetized planets, ordinary stars, neutron stars, and black holes. Pulsar models: processes for slowing down, particle acceleration, and radiation emission; accreting plasmas and x-ray stars; stellar winds; heliosphere and solar wind- relevant magnetic field configuration, measured particle distribution in velocity space and induced collective modes; stability of the current sheet and collisionless processes for magnetic reconnection; theory of collisionless shocks; solitons; Ferroaro-Rosenbluth sheet; solar flare models; heating processes of the solar corona; Earth's magnetosphere (auroral phenomena and their interpretation, bowshock, magnetotail, trapped particle effects); relationship between gravitational (galactic) plasmas and electromagnetic plasmas. 8.913 deals with heliospheric, 8.914 with extra-heliospheric plasmas. | true | Fall | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.914 | Plasma Astrophysics II | For students interested in space physics, astrophysics, and plasma physics in general. Magnetospheres of rotating magnetized planets, ordinary stars, neutron stars, and black holes. Pulsar models: processes for slowing down, particle acceleration, and radiation emission; accreting plasmas and x-ray stars; stellar winds; heliosphere and solar wind- relevant magnetic field configuration, measured particle distribution in velocity space and induced collective modes; stability of the current sheet and collisionless processes for magnetic reconnection; theory of collisionless shocks; solitons; Ferroaro-Rosenbluth sheet; solar flare models; heating processes of the solar corona; Earth's magnetosphere (auroral phenomena and their interpretation, bowshock, magnetotail, trapped particle effects); relationship between gravitational (galactic) plasmas and electromagnetic plasmas. 8.913 deals with heliospheric, 8.914 with extra-heliospheric plasmas. | true | Spring | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.921 | Stellar Structure and Evolution | Observable stellar characteristics; overview of observational information. Principles underlying calculations of stellar structure. Physical processes in stellar interiors; properties of matter and radiation; radiative, conductive, and convective heat transport; nuclear energy generation; nucleosynthesis; and neutrino emission. Protostars; the main sequence, and the solar neutrino flux; advanced evolutionary stages; variable stars; planetary nebulae, supernovae, white dwarfs, and neutron stars; close binary systems; and abundance of chemical elements. | true | Spring | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.942 | Cosmology | Thermal backgrounds in space. Cosmological principle and its consequences: Newtonian cosmology and types of "universes"; survey of relativistic cosmology; horizons. Overview of evolution in cosmology; radiation and element synthesis; physical models of the "early stages." Formation of large-scale structure to variability of physical laws. First and last states. Some knowledge of relativity expected. 8.962 recommended though not required. | false | Fall | Graduate | 3-0-9 | Permission of instructor | null | false | false | false | False | False | False |
8.952 | Particle Physics of the Early Universe | Basics of general relativity, standard big bang cosmology, thermodynamics of the early universe, cosmic background radiation, primordial nucleosynthesis, basics of the standard model of particle physics, electroweak and QCD phase transition, basics of group theory, grand unified theories, baryon asymmetry, monopoles, cosmic strings, domain walls, axions, inflationary universe, and structure formation. | true | Spring | Graduate | 3-0-9 | 8.323; Coreq: 8.324 | null | false | false | false | False | False | False |
8.962 | General Relativity | The basic principles of Einstein's general theory of relativity, differential geometry, experimental tests of general relativity, black holes, and cosmology. | true | Spring | Graduate | 4-0-8 | 8.07, 18.03, and 18.06 | null | false | false | false | False | False | False |
8.971 | Astrophysics Seminar | Advanced seminar on current topics, with a different focus each term. Typical topics: astronomical instrumentation, numerical and statistical methods in astrophysics, gravitational lenses, neutron stars and pulsars. | true | Fall, Spring | Graduate | 2-0-4 [P/D/F] | Permission of instructor | null | false | false | false | False | False | False |
8.972 | Astrophysics Seminar | Advanced seminar on current topics, with a different focus each term. Typical topics: gravitational lenses, active galactic nuclei, neutron stars and pulsars, galaxy formation, supernovae and supernova remnants, brown dwarfs, and extrasolar planetary systems. The presenter at each session is selected by drawing names from a hat containing those of all attendees. Offered if sufficient interest is indicated. | true | Fall, Spring | Graduate | 2-0-4 [P/D/F] | Permission of instructor | null | false | false | false | False | False | False |
8.981, | 8.982 Selected Topics in Astrophysics | Topics of current interest, varying from year to year. Subject not routinely offered; given when sufficient interest is indicated. | true | Spring | Graduate | 3-0-9 [P/D/F] | Permission of instructor | null | false | false | false | False | False | False |
8.995 | Practical Experience in Physics | For Course 8 students participating in off-campus experiences in physics. Before registering for this subject, students must have an internship offer from a company or organization, must identify a Physics advisor, and must receive prior approval from the Physics Department. Upon completion of the project, student must submit a letter from the company or organization describing the work accomplished, along with a substantive final report from the student approved by the MIT advisor. Consult departmental academic office. | true | Fall, IAP, Spring, Summer | Graduate | rranged [P/D/F] | null | null | false | false | false | False | False | False |
8.998 | Teaching and Mentoring MIT Students | Designed for first-time physics mentors and others interested in improving their knowledge and skills in teaching one-on-one and in small groups, particularly TEAL TAs and graduate student TAs. Topics include: cognition, metacognition, and the role of affect; communication skills (practice listening, questioning, and eliciting student ideas); the roles of motivation and mindset in learning; fostering belonging and self-efficacy through peer mentorship; facilitating small-group interactions to enhance peer instruction and learning; physics-specific learning strategies, such as how to teach/learn problem solving; research-based techniques for effective mentorship in STEM. Includes a one-hour class on pedagogy topics, a one-hour weekly Physics Mentoring Community of Practice meeting, and weekly assignments to read or watch material in preparation for class discussions, and written reflections before class. | true | Fall, Spring | Undergraduate | 2-0-1 [P/D/F] | null | null | false | false | false | False | False | False |
8.S301 | Special Subject: Physics | Covers topics in Physics that are not offered in the regular curriculum. Limited enrollment; preference to Physics graduate students. | true | Spring | Graduate | rranged | Permission of instructor | null | false | false | false | False | False | False |
8.S308 | Special Subject: Physics (New) | Opportunity for group study of subjects in physics not otherwise included in the curriculum. | true | IAP | Graduate | rranged | null | null | false | false | false | False | False | False |
8.S372 | Special Subject: Physics | Covers topics in Physics that are not offered in the regular curriculum. | true | Spring | Graduate | 3-0-9 | null | null | false | false | false | False | False | False |
8.S373 | Special Subject: Physics | Covers topics in Physics that are not offered in the regular curriculum. | true | Spring | Graduate | 3-0-9 | null | null | false | false | false | False | False | False |
8.S421 | Special Subject: Physics | Opportunity for group study of subjects in physics not otherwise included in the curriculum. | true | Spring | Graduate | rranged | Permission of instructor | null | false | false | false | False | False | False |
8.S50 | Special Subject: Physics | Opportunity for group study of subjects in physics not otherwise included in the curriculum. | true | IAP | Undergraduate | rranged [P/D/F] | null | null | false | false | false | False | False | False |
8.S998 | Special Subject: Physics | Opportunity for group study of subjects in physics not otherwise included in the curriculum. | true | Fall, Spring | Undergraduate | rranged [P/D/F] | null | null | false | false | false | False | False | False |
8.THG | Graduate Physics Thesis | Program of research leading to the writing of an SM, PhD, or ScD thesis; to be arranged by the student and an appropriate MIT faculty member. | true | Fall, IAP, Spring, Summer | Graduate | rranged | Permission of instructor | null | false | false | false | False | False | False |
9.00 | Introduction to Psychological Science | 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. | true | Spring | Undergraduate | 4-0-8 | null | null | false | false | false | False | Social Sciences | False |
9.01 | Introduction to Neuroscience | 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. | true | Fall | Undergraduate | 4-0-8 | null | null | false | false | true | False | False | False |
9.011 | Systems Neuroscience Core I | 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. | true | Fall | Graduate | 6-0-12 | Permission of instructor | null | false | false | false | False | False | False |
9.012 | Cognitive Science | 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. | true | Spring | Graduate | 6-0-12 | Permission of instructor | null | false | false | false | False | False | False |
9.013[J] | Molecular and Cellular Neuroscience Core II | 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 recommended, though the core subjects can be taken in any sequence. | true | Spring, Spring | Graduate | 3-0-9 | Permission of instructor | 7.68[J] | false | false | false | False | False | False |
9.014 | Quantitative Methods and Computational Models in Neurosciences | 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. | true | Fall | Graduate | 3-1-8 | null | null | false | false | false | False | False | False |
9.015[J] | Molecular and Cellular Neuroscience Core I | 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. | true | Fall | Graduate | 3-0-9 | null | 7.65[J] | false | false | false | False | False | False |
9.016[J] | Introduction to Sound, Speech, and Hearing | Introduces students to the acoustics, anatomy, physiology, and mechanics related to speech and hearing. Focuses on how humans generate and perceive speech. Topics related to speech, explored through applications and challenges involving acoustics, speech recognition, and speech disorders, include acoustic theory of speech production, basic digital speech processing, control mechanisms of speech production and basic elements of speech and voice perception. Topics related to hearing include acoustics and mechanics of the outer ear, middle ear, and cochlea, how pathologies affect their function, and methods for clinical diagnosis. Surgical treatments and medical devices such as hearing aids, bone conduction devices, and implants are also covered. | true | Fall | Graduate | 4-0-8 | (6.3000 and 8.03) or permission of instructor | HST.714[J] | false | false | false | False | False | False |
9.017 | Systems Neuroscience Core II | 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. | true | Spring | Graduate | 2-2-8 | 18.06 or (9.011 and 9.014) | null | false | false | false | False | False | False |
9.021[J] | Cellular Neurophysiology and Computing | Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. | true | Spring | Graduate | 5-2-5 | (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor | 2.794[J], 6.4812[J], 20.470[J], HST.541[J] | false | false | false | False | False | False |
9.07 | Statistics for Brain and Cognitive Science | 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. | true | Fall | Undergraduate | 4-0-8 | 6.100B | null | false | false | false | False | False | False |
9.073[J] | Statistics for Neuroscience Research | 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. | true | Spring | Graduate | 3-0-9 | Permission of instructor | HST.460[J] | false | false | false | False | False | False |
9.09[J] | Cellular and Molecular Neurobiology | Introduction to the structure and function of the nervous system. Emphasizes the cellular properties of neurons and other excitable cells. Includes the structure and biophysical properties of excitable cells, synaptic transmission, neurochemistry, neurodevelopment, integration of information in simple systems, and detection and information coding during sensory transduction. | true | Spring | Undergraduate | 4-0-8 | 7.05 or 9.01 | 7.29[J] | false | false | false | False | False | False |
9.110[J] | Nonlinear Control | Introduction to nonlinear control and estimation in physical and biological systems. Nonlinear stability theory, Lyapunov analysis, Barbalat's lemma. Feedback linearization, differential flatness, internal dynamics. Sliding surfaces. Adaptive nonlinear control and estimation. Multiresolution bases, nonlinear system identification. Contraction analysis, differential stability theory. Nonlinear observers. Asynchronous distributed computation and learning. Concurrent synchronization, polyrhythms. Monotone nonlinear systems. Emphasizes application to physical systems (robots, aircraft, spacecraft, underwater vehicles, reaction-diffusion processes, machine vision, oscillators, internet), machine learning, computational neuroscience, and systems biology. Includes term projects. | true | Spring | Graduate | 3-0-9 | 2.151, 6.7100, 16.31, or permission of instructor | 2.152[J] | false | false | false | False | False | False |
9.12 | Experimental Molecular Neurobiology | 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. | true | Spring | Undergraduate | 2-4-6 | Biology (GIR) and 9.01 | null | true | false | false | False | False | False |
9.123[J] | Neurotechnology in Action | 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. | true | Spring | Graduate | 3-6-3 | Permission of instructor | 20.203[J] | false | false | false | False | False | False |
9.13 | The Human Brain | 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. | true | Spring | Undergraduate | 3-0-9 | 9.00, 9.01, or permission of instructor | null | false | false | false | False | False | False |
9.17 | Systems Neuroscience Laboratory | 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. | true | Fall | Undergraduate | 2-4-6 | 9.01 or permission of instructor | null | true | false | false | False | False | False |
9.175[J] | Robotics | Introduction to robotics and learning in machines. Kinematics and dynamics of rigid body systems. Adaptive control, system identification, sparse representations. Force control, adaptive visual servoing. Task planning, teleoperation, imitation learning. Navigation. Underactuated systems, approximate optimization and control. Dynamics of learning and optimization in networks. Elements of biological planning and control. Motor primitives, entrainment, active sensing, binding models. Term projects. | true | Fall | Graduate | 3-0-9 | 2.151 or permission of instructor | 2.165[J] | false | false | false | False | False | False |
9.18[J] | Developmental Neurobiology | 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. | true | Spring | Undergraduate | 3-0-9 | 7.03, 7.05, 9.01, or permission of instructor | 7.49[J] | false | false | false | False | False | False |
9.181[J] | Developmental Neurobiology | 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. | true | Spring | Graduate | 3-0-9 | 9.011 or permission of instructor | 7.69[J] | false | false | false | False | False | False |
9.19 | Computational Psycholinguistics | 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. | true | Fall | Undergraduate | 4-0-8 | (6.100B and (6.3700, 9.40, or 24.900)) or permission of instructor | null | false | false | false | False | False | False |
9.190 | Computational Psycholinguistics | 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. | true | Fall | Graduate | 4-0-8 | (6.100B and (6.3702, 9.40, or 24.900)) or permission of instructor | null | false | false | false | False | False | False |
9.21[J] | Cellular Neurophysiology and Computing | Integrated overview of the biophysics of cells from prokaryotes to neurons, with a focus on mass transport and electrical signal generation across cell membrane. First third of course focuses on mass transport through membranes: diffusion, osmosis, chemically mediated, and active transport. Second third focuses on electrical properties of cells: ion transport to action potential generation and propagation in electrically excitable cells. Synaptic transmission. Electrical properties interpreted via kinetic and molecular properties of single voltage-gated ion channels. Final third focuses on biophysics of synaptic transmission and introduction to neural computing. Laboratory and computer exercises illustrate the concepts. Students taking graduate version complete different assignments. Preference to juniors and seniors. | true | Spring | Undergraduate | 5-2-5 | (Physics II (GIR), 18.03, and (2.005, 6.2000, 6.3000, 10.301, or 20.110)) or permission of instructor | 2.791[J], 6.4810[J], 20.370[J] | false | false | false | False | False | False |
9.24 | Disorders and Diseases of the Nervous System | 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. | true | Spring | Undergraduate | 3-0-9 | (7.29 and 9.01) or permission of instructor | null | false | false | false | False | False | False |
9.26[J] | Principles and Applications of Genetic Engineering for Biotechnology and Neuroscience | 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. | true | Spring | Undergraduate | 3-0-9 | Biology (GIR) | 20.205[J] | false | false | false | False | False | False |
9.271[J] | Pioneering Technologies for Interrogating Complex Biological Systems | Introduces pioneering technologies in biology and medicine and discusses their underlying biological/molecular/engineering principles. Topics include emerging sample processing technologies, advanced optical imaging modalities, and next-gen molecular phenotyping techniques. Provides practical experience with optical microscopy and 3D phenotyping techniques. Limited to 15. | true | Spring | Graduate | 3-0-9 | null | 10.562[J], HST.562[J] | false | false | false | False | False | False |
9.272[J] | Topics in Neural Signal Processing | 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. | false | Spring | Graduate | 3-0-9 | Permission of instructor | HST.576[J] | false | false | false | False | False | False |
9.285[J] | Audition: Neural Mechanisms, Perception and Cognition | 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. | true | Spring | Graduate | 6-0-6 | Permission of instructor | HST.723[J] | false | false | false | False | False | False |
9.301[J] | Neural Plasticity in Learning and Memory | 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. | true | Spring | Graduate | 3-0-9 | Permission of instructor | 7.98[J] | false | false | false | False | False | False |
9.32 | Genes, Circuits, and Behavior | 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. | true | Spring | Undergraduate | 3-0-9 | 7.29, 9.16, 9.18, or permission of instructor | null | false | false | false | False | False | False |
9.34[J] | Biomechanics and Neural Control of Movement | Presents a quantitative description of how biomechanical and neural factors interact in human sensory-motor behavior. Students survey recent literature on how motor behavior is controlled, comparing biological and robotic approaches to similar tasks. Topics may include a review of relevant neural, muscular and skeletal physiology, neural feedback and "equilibrium-point" theories, co-contraction strategies, impedance control, kinematic redundancy, optimization, intermittency, contact tasks and tool use. Students taking graduate version complete additional assignments. | true | Spring | Graduate | 3-0-9 | 2.004 or permission of instructor | 2.183[J] | false | false | false | False | False | False |
9.35 | Perception | 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. | true | Spring | Undergraduate | 4-0-8 | 9.01 or permission of instructor | null | false | false | false | False | False | False |
9.357 | Current Topics in Perception | 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. | true | Spring | Graduate | 2-0-7 | Permission of instructor | null | false | false | false | False | False | False |
9.36 | Neurobiology of Self | 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. | true | Fall | Undergraduate | 3-0-9 | 9.01 | null | false | false | false | False | False | False |
9.360 | Neurobiology of Self | 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. | true | Fall | Graduate | 3-0-9 | 9.01 | null | false | false | false | False | False | False |
9.39 | Language in the Mind and Brain | 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. | false | Spring | Undergraduate | 3-0-9 | 9.00, 9.01, or permission of instructor | null | false | false | false | False | False | False |
9.390 | Language in the Mind and Brain | 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. | true | Spring | Graduate | 3-0-9 | 9.00, 9.01, or permission of instructor | null | false | false | false | False | False | False |
9.40 | Introduction to Neural Computation | 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. | true | Spring | Undergraduate | 4-0-8 | (Physics II (GIR), 6.100B, and 9.01) or permission of instructor | null | false | false | false | False | False | False |
9.401 | Tools for Robust Science | 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. | true | Spring | Graduate | 3-0-9 | null | null | false | false | false | False | False | False |
9.41 | Research and Communication in Neuroscience and Cognitive Science | 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. | true | Fall | Undergraduate | 2-12-4 | 9.URG and permission of instructor | null | false | false | false | False | False | False |
9.42 | The Brain and Its Interface with the Body | 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. | true | Spring | Undergraduate | 3-0-9 | 7.28, 7.29, or permission of instructor | null | false | false | false | False | False | False |
9.422[J] | Principles of Neuroengineering | Covers how to innovate technologies for brain analysis and engineering, for accelerating the basic understanding of the brain, and leading to new therapeutic insight and inventions. Focuses on using physical, chemical and biological principles to understand technology design criteria governing ability to observe and alter brain structure and function. Topics include optogenetics, noninvasive brain imaging and stimulation, nanotechnologies, stem cells and tissue engineering, and advanced molecular and structural imaging technologies. Includes design projects. Designed for students with engineering maturity who are ready for design. Students taking graduate version complete additional assignments. | true | Fall | Graduate | 3-0-9 | Permission of instructor | 20.452[J], MAS.881[J] | false | false | false | False | False | False |
9.455[J] | Revolutionary Ventures: How to Invent and Deploy Transformative Technologies | Seminar on envisioning and building ideas and organizations to accelerate engineering revolutions. Focuses on emerging technology domains, such as neurotechnology, imaging, cryotechnology, gerontechnology, and bio-and-nano fabrication. Draws on historical examples as well as live case studies of existing or emerging organizations, including labs, institutes, startups, and companies. Goals range from accelerating basic science to developing transformative products or therapeutics. Each class is devoted to a specific area, often with invited speakers, exploring issues from the deeply technical through the strategic. Individually or in small groups, students prototype new ventures aimed at inventing and deploying revolutionary technologies. | true | Fall | Graduate | 2-0-7 | Permission of instructor | 15.128[J], 20.454[J], MAS.883[J] | false | false | false | False | False | False |
9.48[J] | Philosophical Issues in Brain Science | An introduction to some central philosophical questions about the mind, specifically those intimately connected with contemporary psychology and neuroscience. Discussions focus on arguments over innate concepts; 'mental images' as pictures in the head; whether color is in the mind or in the world; and whether there can be a science of consciousness. Explains the relevant parts of psychology and neuroscience as the subject proceeds. | true | Fall | Undergraduate | 3-0-9 | null | 24.08[J] | false | false | false | False | Humanities | CI-H |
9.49 | Neural Circuits for Cognition | 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. | true | Fall | Undergraduate | 3-0-9 | 9.40, 18.06, or permission of instructor | null | false | false | false | False | False | False |
9.490 | Neural Circuits for Cognition | 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. | true | Fall | Graduate | 3-0-9 | 9.40, 18.06, or permission of instructor | null | false | false | false | False | False | False |
9.50 | Research in Brain and Cognitive Sciences | 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 advisor and the undergraduate faculty officer. Written presentation of results is required. | true | Fall, Spring | Undergraduate | 0-12-0 | 9.00 and permission of instructor | null | false | false | false | False | False | False |
9.520[J] | Statistical Learning Theory and Applications | 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. | true | Fall | Graduate | 3-0-9 | 6.3700, 6.7900, 18.06, or permission of instructor | 6.7910[J] | false | false | false | False | False | False |
9.521[J] | Mathematical Statistics: a Non-Asymptotic Approach | 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 | true | Spring | Graduate | 3-0-9 | (6.7700, 18.06, and 18.6501) or permission of instructor | 18.656[J], IDS.160[J] | false | false | false | False | False | False |
9.522 | Statistical Reinforcement Learning | 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. | true | Fall | Graduate | 9-0-3 | null | null | false | false | false | False | False | False |
9.53 | Emergent Computations Within Distributed Neural Circuits | 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. | true | Spring | Undergraduate | 4-0-8 | 9.40 or permission of instructor | null | false | false | false | False | False | False |
9.530 | Emergent Computations Within Distributed Neural Circuits | 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. | true | Spring | Graduate | 4-0-8 | 9.40 or permission of instructor | null | false | false | false | False | False | False |
9.55[J] | Consumer Behavior | Examines the behavior of consumers through the lens of behavioral economics, cognitive science, and social psychology. Reviews theory and research and brings this knowledge to bear on a wide range of applications in business and public policy. Lectures are combined with cases, guest speakers, and brainstorming sessions where students work in teams to apply concepts to real-world problems. Meets with 15.847 when offered concurrently. Expectations and evaluation criteria may differ for students taking the graduate version; consult syllabus or instructor for specific details. | true | Fall | Undergraduate | 3-0-6 | null | 15.8471[J] | false | false | false | False | False | False |
9.550[J] | Consumer Behavior | Examines the behavior of consumers through the lens of behavioral economics, cognitive science, and social psychology. Reviews theory and research and brings this knowledge to bear on a wide range of applications in business and public policy. Lectures are combined with cases, guest speakers, and brainstorming sessions where students work in teams to apply concepts to real-world problems. Meets with 15.8471 when offered concurrently. Expectations and evaluation criteria may differ for students taking the graduate version; consult syllabus or instructor for specific details. | true | Fall | Graduate | 3-0-6 | 15.809, 15.814, or permission of instructor | 15.847[J] | false | false | false | False | False | False |
9.58 | Projects in the Science of Intelligence | 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. | true | Fall | Undergraduate | 3-0-9 | (6.3900 and (9.40 or 18.06)) or permission of instructor | null | false | false | false | False | False | False |
9.583[J] | Functional Magnetic Resonance Imaging: Data Acquisition and Analysis | Provides background necessary for designing, conducting, and interpreting fMRI studies in the human brain. Covers in depth the physics of image encoding, mechanisms of anatomical and functional contrasts, the physiological basis of fMRI signals, cerebral hemodynamics, and neurovascular coupling. Also covers design methods for stimulus-, task-driven and resting-state experiments, as well as workflows for model-based and data-driven analysis methods for data. Instruction in brain structure analysis and surface- and region-based analyses. Laboratory sessions include data acquisition sessions at the 3 Tesla MRI scanner at MIT and the Connectom and 7 Tesla scanners at the MGH/HST Martinos Center, as well as hands-on data analysis workshops. Introductory or college-level neurobiology, physics, and signal processing are helpful. | false | Fall | Graduate | 2-3-7 | 18.05 and (18.06 or permission of instructor) | HST.583[J] | false | false | false | False | False | False |
9.59[J] | Laboratory in Psycholinguistics | 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. | true | Spring | Undergraduate | 3-3-6 | null | 24.905[J] | true | false | false | False | False | False |
9.60 | Machine-Motivated Human Vision | 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. | true | Spring | Undergraduate | 2-1-9 | null | null | true | false | false | False | False | False |
9.611[J] | Natural Language and the Computer Representation of Knowledge | Explores the relationship between the computer representation and acquisition of knowledge and the structure of human language, its acquisition, and hypotheses about its differentiating uniqueness. Emphasizes development of analytical skills necessary to judge the computational implications of grammatical formalisms and their role in connecting human intelligence to computational intelligence. Uses concrete examples to illustrate particular computational issues in this area. | true | Spring | Graduate | 3-3-6 | 6.4100 or permission of instructor | 6.8630[J], 24.984[J] | false | false | false | False | False | False |
9.66[J] | Computational Cognitive Science | 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. | true | Fall, Fall | Graduate | 3-0-9 | 6.3700, 6.3800, 9.40, 18.05, 6.3900, or permission of instructor | 6.4120[J] | false | false | false | False | False | False |
Subsets and Splits