Neurosciences

[Updated 10/20/2010]

This concentration combines biological, cognitive, and computational approaches to neuroscience and neural modeling. Students should choose a total of six courses from at least three of the following six areas, with at least three courses coming from among the first two areas. It is recommended that students who are interested in computational approaches take CS 221 to satisfy the core AI requirement. Practical exposure to laboratory research in neuroscience is also highly recommended. See also the Stanford Neuroscience Program website. You may also be interested in the Brain/CS reading group.

1. Basic neuroscience. For example: Molecular and Cellular Neurobiology (Bio 154/254/NBio 254), Human Behavioral Biology (Bio 150/250/HumBio 160), The Nervous System (Nbio 206)*, Introduction to Brain and Behavior (Bio 20/HumBio 21), Cellular Neuroscience: Cell Signaling and Behavior (Bio 153/Psych 120), Ion Transport and Intracellular Messengers (Psych 121/228).
2. Systems neuroscience. For example: Developmental Neurobiology (Bio 158), Neural Systems and Behavior (Bio 163/263/HumBio 163), Exploring Neural Circuits (Bio 222), Neural Basis of Behavior (Nbio 218), Introduction to Perception (Psych 30), Introduction to Learning and Memory (Psych 45), Introduction to Cognitive Neuroscience (Psych 50) [if not taken for the core], Developmental Anomalies (Psych 143), Brain and Decision Making (Psych 232), Affective Neuroscience (Psych 251), Topics in Cognitive Control (Psych 279).
3. Computational approaches. For example: Computational Neuroimaging (Psych 204A), Computational Neuroimaging: Analysis Methods (Psych 204B), Introduction to Robotics (CS 223A), Machine Learning (CS 229), Systems Biology (Bioc 278/Bioe310/CS 278/CSB 278), Adaptive Neural Networks (EE 373B), Topics in Neuroengineering (EE 418), Computational Neuroscience (NENS 220), The Neural Basis of Cognition: A Parallel Distributed Processing Approach (Psych 209A), Applications of Parallel Distributed Processing Models to Cognition and Cognitive Neuroscience (Psych 209B).
4. Biological and computational approaches to vision. For example: Introduction to Computer Vision (CS 223B), Introduction to Perception (Psych 30), Applied Vision and Image Systems (Psych 221), High-Level Vision (Psych 250).
5. Philosophical and theoretical approaches. Philosophy of Mind (Phil 186), Topics in the Philosophy of Neuroscience (Symsys 206).
6. Methodological foundations. For example: Mathematical Methods for Robotics, Vision, and Graphics (CS 205A), Linear Algebra and Matrix Theory (Math 113), Statistical Methods for Behavioral and Social Sciences (Psych 252), Research Methods and Experimental Design (Psych 110), Biostatistics (Stats 141), Introduction to Applied Statistics (Stats 191), Introduction to Statistical Inference (Stats 200).

* Note: Nbio 206 is an 7-8-unit course which counts as two concentration courses, from areas 1 and 2.