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Course 9: Brain and Cognitive Sciences |
 | | | 9.00-9.499 | | | 9.50-9.999 plus Thesis, UROP | | |  |
9.50 Research in Brain and Cognitive Sciences
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Prereq: 9.00 and permission of instructor Units: 0-12-0  TBA.
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. S. Vallin No textbook information available 9.520[J] Statistical Learning Theory and Applications
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(Same subject as 6.7910[J]) Prereq: 6.3700, 6.7900, 18.06, or permission of instructor Units: 3-0-9 Lecture: TR11-12.30 (46-3002)
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 No textbook information available 9.521[J] Mathematical Statistics: a Non-Asymptotic Approach
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(Same subject as 18.656[J], IDS.160[J]) Prereq: (6.7700, 18.06, and 18.6501) or permission of instructor Units: 3-0-9
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
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Prereq: None Units: 9-0-3 Lecture: TR11-12.30 (46-3002)
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. A. Rakhlin No textbook information available 9.53 Emergent Computations Within Distributed Neural Circuits
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(Subject meets with 9.530) Prereq: 9.40 or permission of instructor Units: 4-0-8
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. R. Ajemian 9.530 Emergent Computations Within Distributed Neural Circuits
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(Subject meets with 9.53) Prereq: 9.40 or permission of instructor Units: 4-0-8
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. Staff 9.55[J] Consumer Behavior
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(Same subject as 15.8471[J]) Prereq: None Units: 3-0-6 Credit cannot also be received for 9.550, 15.847
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. Staff 9.550[J] Consumer Behavior
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(Same subject as 15.847[J]) Prereq: 15.809, 15.814, or permission of instructor Units: 3-0-6 Credit cannot also be received for 9.55, 15.8471
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. Staff 9.58 Projects in the Science of Intelligence
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Prereq: (6.3900 and (9.40 or 18.06)) or permission of instructor Units: 3-0-9 Lecture: W1.30-3.30 (46-3189) Recitation: M2 (46-3189)
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 No textbook information available 9.583[J] Functional Magnetic Resonance Imaging: Data Acquisition and Analysis
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(Same subject as HST.583[J]) Prereq: 18.05 and (18.06 or permission of instructor) Units: 2-3-7 Lecture: MW3 (3-333) Lab: M12.30-2 (3-333) Recitation: W2 (3-333) +final
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. J. Polimeni, A. Yendiki, J. Chen Textbooks (Fall 2024) 9.59[J] Laboratory in Psycholinguistics
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(Same subject as 24.905[J]) Prereq: None Units: 3-3-6
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. E. Gibson 9.60 Machine-Motivated Human Vision
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Prereq: None Units: 2-1-9
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. P. Sinha 9.611[J] Natural Language and the Computer Representation of Knowledge
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(Same subject as 6.8630[J], 24.984[J]) Prereq: 6.4100 or permission of instructor Units: 3-3-6
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. Staff 9.66[J] Computational Cognitive Science
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(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 Units: 3-0-9 9.66: Lecture: TR1-2.30 (46-3002)
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 9.66: No textbook information available 9.660 Computational Cognitive Science
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(Subject meets with 6.4120[J], 9.66[J]) Prereq: Permission of instructor Units: 3-0-9 9.660: Lecture: TR1-2.30 (46-3002)
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. J. Tenenbaum 9.660: No textbook information available 9.67[J] Materials Physics of Neural Interfaces
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(Same subject as 3.056[J]) (Subject meets with 3.64[J], 9.670[J]) Prereq: 3.033 or permission of instructor Units: 3-0-9
Builds a foundation of physical principles underlying electrical, optical, and magnetic approaches to neural recording and stimulation. Discusses neural recording probes and materials considerations that influence the quality of the signals and longevity of the probes in the brain. Students then consider physical foundations for optical recording and modulation. Introduces magnetism in the context of biological systems. Focuses on magnetic neuromodulation methods and touches upon magnetoreception in nature and its physical limits. Includes team projects that focus on designing electrical, optical, or magnetic neural interface platforms for neuroscience. Concludes with an oral final exam consisting of a design component and a conversation with the instructor. Students taking graduate version complete additional assignments. P. Anikeeva 9.670[J] Materials Physics of Neural Interfaces
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(Same subject as 3.64[J]) (Subject meets with 3.056[J], 9.67[J]) Prereq: Permission of instructor Units: 3-0-9
Builds a foundation of physical principles underlying electrical, optical, and magnetic approaches to neural recording and stimulation. Discusses neural recording probes and materials considerations that influence the quality of the signals and longevity of the probes in the brain. Students then consider physical foundations for optical recording and modulation. Introduces magnetism in the context of biological systems. Focuses on magnetic neuromodulation methods and touches upon magnetoreception in nature and its physical limits. Includes team projects that focus on designing electrical, optical, or magnetic neural interface platforms for neuroscience. Concludes with an oral final exam consisting of a design component and a conversation with the instructor. Students taking graduate version complete additional assignments. Staff 9.72 Vision in Art and Neuroscience
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(Subject meets with 9.720) Prereq: None Units: 2-2-8 Lecture: T3-5 (10-150) Lab: R3-5 (10-150)
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 No textbook information available 9.720 Vision in Art and Neuroscience
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(Subject meets with 9.72) Prereq: None Units: 2-2-8 Lecture: T3-5 (10-150) Lab: R3-5 (10-150)
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 No textbook information available 9.822[J] Psychology and Economics
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(Same subject as 14.137[J]) Prereq: None Units: 4-0-8
Examines "psychology appreciation" for economics students. Aims to enhance knowledge and intuition about psychological processes in areas relevant to economics. Increases understanding of psychology as an experimental discipline, with its own distinct rules and style of argument. Topics include self-knowledge, cognitive dissonance, self-deception, emotions, social norms, self-control, learning, mental accounting, memory, individual and group behavior, and some personality and psycho-analytic models. Within each of these topics, we showcase effective and central experiments and discuss their role in the development of psychological theory. Term paper required. D. Prelec 9.830 Graduate Student Internship
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Prereq: None Units arranged TBA.
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. J. Ormerod No textbook information available 9.85 Infant and Early Childhood Cognition
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Prereq: 9.00 Units: 3-0-9 Lecture: TR1-2.30 (46-3189)
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. L. Schulz No textbook information available 9.89 Off-Campus Undergraduate Research in Brain and Cognitive Sciences
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Prereq: None Units arranged TBA.
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 advisor will provide an evaluation of the student's work. The student must also submit a write-up of the experience, approved by the MIT advisor. T. Tomic No textbook information available 9.90 Practical Experience in Brain and Cognitive Sciences
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Prereq: Permission of instructor Units: 0-1-0 [P/D/F]
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 advisor. Staff No textbook information available 9.900 Clinical Connection Module
(, ) Not offered regularly; consult department Prereq: None. Coreq: 9.011, 9.012, 9.013, 9.014, or 9.015; permission of instructor Units: 0-1-0 [P/D/F]
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. Fall: Theresa Tomic Spring: Theresa Tomic 9.901 Responsible Conduct in Science
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Prereq: None Units: 1-0-1 [P/D/F]
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. Staff 9.91 Independent Study in Brain and Cognitive Sciences
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Prereq: 9.00, two additional subjects in Brain and Cognitive Sciences, and permission of instructor Units arranged TBA.
Individual study of a topic under the direction of a member of the faculty. T. Tomic No textbook information available 9.918 BCS Grant Writing Workshop
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Prereq: None Units: 1-0-0 TBA.
Fellowship writing workshop to develop applications for predoctoral fellowships, including the NSF and NDSEG programs. Kanwisher, Nancy No textbook information available 9.919 Teaching Brain and Cognitive Sciences
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Prereq: None Units arranged TBA.
For teaching assistants in Brain and Cognitive Sciences, in cases where teaching assignment is approved for academic credit by the department. Fall: catalog-help@mit.edu Spring: catalog-help@mit.edu No textbook information available 9.921 Research in Brain and Cognitive Sciences
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Prereq: Permission of instructor Units arranged TBA.
Guided research under the sponsorship of individual members of the faculty. Ordinarily restricted to candidates for the doctoral degree in Course 9. J. Ormerod No textbook information available 9.941 Graduate Thesis Proposal
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Prereq: Permission of instructor Units arranged [P/D/F] TBA.
Students submit written proposals for thesis according to stated deadlines. J. Ormerod No textbook information available 9.980[J] Leadership and Professional Strategies & Skills Training (LEAPS), Part I: Advancing Your Professional Strategies and Skills
(); second half of term
(Same subject as 5.961[J], 8.396[J], 12.396[J], 18.896[J]) Prereq: None Units: 2-0-1 [P/D/F]
Part I (of two parts) of the LEAPS graduate career development and training series. Topics include: navigating and charting an academic career with confidence; convincing an audience with clear writing and arguments; mastering public speaking and communications; networking at conferences and building a brand; identifying transferable skills; preparing for a successful job application package and job interviews; understanding group dynamics and different leadership styles; leading a group or team with purpose and confidence. Postdocs encouraged to attend as non-registered participants. Limited to 80. A. Frebel 9.981[J] Leadership and Professional Strategies & Skills Training (LEAPS), Part II: Developing Your Leadership Competencies
(); first half of term
(Same subject as 5.962[J], 8.397[J], 12.397[J], 18.897[J]) Prereq: None Units: 2-0-1 [P/D/F]
Part II (of two parts) of the LEAPS graduate career development and training series. Topics covered include gaining self awareness and awareness of others, and communicating with different personality types; learning about team building practices; strategies for recognizing and resolving conflict and bias; advocating for diversity and inclusion; becoming organizationally savvy; having the courage to be an ethical leader; coaching, mentoring, and developing others; championing, accepting, and implementing change. Postdocs encouraged to attend as non-registered participants. Limited to 80. D. Rigos 9.990 Professional Development
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| | | 9.00-9.499 | | | 9.50-9.999 plus Thesis, UROP | | | |
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