A total of 16 courses are required for the concentration. Each student is required to pass 9 courses designed to introduce students to the foundations, systems level, and integrative aspects which uniquely define cognitive neuroscience; two laboratory courses; four elective courses; and either a senior seminar course (CG 195) or an independent research course. The laboratory and elective courses should fit within a particular theme or category such as general cognition, vision, language or computational/modeling. The design of the concentration and selection of courses should be made in consultation with the faculty advisor.
Foundation Courses
a) BN1 Introduction to Neuroscience
b) MA9 Introductory Calculus (or the equivalent)
c) BI20 The Foundation of Living Systems
d) CG/PY 9 Quantitative Methods in Psychology, AM165 Statistical Inference,
PY206 Experimental Design, or BC213 Principals of Biostatistics and
Data Analysis. Note: Students wishing to purse a computational/modeling track are encouraged
to take AM165
e) CG42 Human Cognition
Systems Level and Integrative Courses
a) PY47 Brain Damage and the Mind
b) BN103 Neural Systems
c) BN166 Cognitive Neuroscience
d) CG128 Computational Cognitive Science or AM40 Mathematical Methods in the
Brain Sciences
Laboratory Courses
Students must choose two laboratory courses. Please note that due to
enrollment limits in some lab courses, priority may be given to concentrators
in that department. Students should therefore be prepared to choose
from the other laboratory options.
BN160 Experimental Neurobiology
BN165 Structure of the Nervous System
BN167 Neuropharmacology and Synaptic Transmission
BN168 Computational Neuroscience
PY103 Techniques in Physiological Psychology
PY119 Human Sensory Processing
PY120 Animal Learning and Behavior Laboratory
CG102 Neural Modeling Laboratory
CG124 Research Methods in Physiologic and Acoustic Phonetics
CG145 Research in Psycholinguistics
CG/PY153 Laboratory in Cognitive Processes
CG198; BN195, 196; or PY 199 Independent Study (can be used for only
ONE laboratory)
Electives
Four additional courses around a particular theme. Normally only one
elective course that is below the 100 level may count towards the elective
courses required. An appropriate (but additional) laboratory course
may be used in lieu of one of the four elective courses.
Primarily behavioral/experimental
CG32 Biology and Evolution of Language
CG/PY44 Perception and Mind
CG45 Language and the Mind
CG48 Human Thinking and Problem-Solving
CG50 Making Decisions
CG/PY63 Children's Thinking: The Nature of Cognitive Development
CG120 Computational Vision
CG123 Production, Perception, and Analysis of Speech
CG138 Ecological Approach to Perception and Action
CG141 Language Processing
CG142 Syntactic Theory and Syntactic Processing
CG143 Child Language Acquisition
CG144 Visualizing Vision
CG147 Language Learning Disorders
CG148 Language and the Brain
CG150 Subcortical Bases of Language and Thought
CG/PY152 Thinking
CG154 The Evolution of Perceptual System
CG156 Human Memory and Learning
CG162 Cognitive Development
CG174 Topics in Language Acquisition
CG186 Topics in Cognitive Science
CG187 Concepts and Categories
BN65 Biology of Hearing
BN66 Biology of Vision
BN168 Computational Neuroscience
PY27 Basic Perception
PY44 Perception and Mind
PY75 Principles of Behavioral Neuroscience
PY81 Child Development
PY94 Developmental Psyhopathology
PY101 Psychopharmacology
PY102 Psychophysiology of Sleep and Dreams
PY105 Music and Mind
PY140 Human Memory
PY178 Psychological Acoustics
PY179 Psychology of Timing
PY180 Animal Cognition
PY181 Seminar in Cognitive Neuroscience
PY182 Cognitive Neuroscience of Emotion
PY184 Functional Magnetic Resonance Imaging: Theory and Practice
PY185 Motion Perception
BI45 Animal Behavior
Primarily Computational/Modeling
Students interested in computational/modeling approaches to cognitive
neuroscience are advised to take AM 33 & 34 (Methods of Applied
Analysis I & II). Note that MA 10 is a prerequisite for these courses.
BN168 Computational Neuroscience
AM10 Introduction to Modeling
AM136 Topics in Chaotic Dynamics
CG136/CS146 Introduction to Computational Linguistics
CS141 Introduction to Artificial Intelligence
CS148 Building Intelligent Robots
EN122 Neuroengineering
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