Computation and Neural Systems
CNS 100. Introduction to Computation and Neural Systems. 1 unit; first term. This course is designed to introduce undergraduate and first-year CNS graduate students to the wide variety of research being undertaken by CNS faculty. Topics from all the CNS research labs are discussed and span the range from biology to engineering. Graded pass/fail. Instructor: Perona.
CNS/SS/Psy/Bi 102 ab. Brains, Minds, and Society. 9 units (3-0-6); second, third terms. Prerequisites: Bi/CNS/NB 150 and CNS/Bi/Ph/CS/NB 187, or instructor’s permission. Introduction to the computations made by the brain during economic and social decision making and their neural substrates. First quarter: Reinforcement learning. Unconscious and conscious processing. Emotion. Behavioral economics. Goal-directed and habit learning. Facial processing in social neuroscience. Second quarter: History and mechanisms of reinforcement. Associative learning. Mentalizing and strategic thinking. Neural basis of prosociality. Exploration-exploitation tradeoff. Functions of basal ganglia. Instructors: Camerer, O’Doherty.
Psy/CNS 105 ab. Frontiers in Neuroeconomics. 5 units (1.5-0-3.5). For course description, see Psychology.
CNS/SS/Psy 110 ab. Cognitive Neuroscience Tools. 9 units (3-0-6); second, third terms. This course covers tools and statistical methods used in cognitive neuroscience research. Topics vary from year to year depending on the interests of the students. Recent topics include statistical modeling for fMRI data, experimental design for fMRI, and the preprocessing of fMRI data. Not Offered 2017-2018. Instructor: Rangel.
Psy/CNS 130. Introduction to Human Memory. 9 units (3-0-6). For course description, see Psychology.
CNS/Psy/Bi 131. The Psychology of Learning and Motivation. 9 units (3-0-6); second term. This course will serve as an introduction to basic concepts, findings, and theory from the field of behavioral psychology, covering areas such as principles of classical conditioning, blocking and conditioned inhibition, models of classical conditioning, instrumental conditioning, reinforcement schedules, punishment and avoidance learning. The course will track the development of ideas from the beginnings of behavioral psychology in the early 20th century to contemporary learning theory. Not offered 2017–18.
EE/CNS/CS 148. Selected Topics in Computational Vision. 9 units (3-0-6); third term. For course description, see Electrical Engineering.
Bi/CNS/NB 150. Introduction to Neuroscience. 10 units (4-0-6). For course description, see Biology.
Bi/CNS/NB 152. Neural Circuits and Physiology of Appetite and Body Homeostasis. 6 units (2-0-4); spring. For course description, see Biology
Bi/CNS/NB 153. Brain Circuits. 9 units (3-0-6); Second Term. Prerequisites: Bi/CNS/NB 150 or equivalent. For course description, see Biology.
CMS/CS/CNS/EE 155. Machine Learning Data Mining. 12 units (3-3-6); second term. For course description, see Computing and Mathematical Sciences.
CS/CNS/EE 156 ab. Learning Systems. 9 units (3-0-6). For course description, see Computer Science.
Bi/CNS/NB 157. Comparative Nervous Systems. 9 units (2-3-4); third term. For course description, see Biology.
Bi/CNS 158. Vertebrate Evolution. 9 units (3-0-6); third term. For course description, see Biology.
CS/CNS/EE 159. Advanced Topics in Machine Learning. 9 units (3-0-6); third term. For course description, see Computer Science.
Bi/CNS/NB 162. Cellular and Systems Neuroscience Laboratory. 12 units (2-7-3); third term. Prerequisites: Bi/CNS/NB 150 or instructor’s permission. For course description, see Biology.
Bi/CNS/NB 164. Tools of Neurobiology. 9 units (3-0-6); second term. Prerequisites: Bi/CNS/NB 150 or equivalent. For course description, see Biology.
CS/CNS 171. Introduction to Computer Graphics Laboratory. 12 units (3-6-3). For course description, see Computer Science.
CS/CNS 174. Computer Graphics Projects. 12 units (3-6-3). For course description, see Computer Science.
CNS/Bi/SS/Psy/NB 176. Cognition. 9 units (4-0-5); third term. The cornerstone of current progress in understanding the mind, the brain, and the relationship between the two is the study of human and animal cognition. This course will provide an in-depth survey and analysis of behavioral observations, theoretical accounts, computational models, patient data, electrophysiological studies, and brain-imaging results on mental capacities such as attention, memory, emotion, object representation, language, and cognitive development. Instructor: Shimojo.
CNS 180. Research in Computation and Neural Systems. Units by arrangement with faculty. Offered to precandidacy students.
Bi/CNS/NB 184. The Primate Visual System. 9 units (3-1-5). For course description, see Biology.
Bi/CNS/NB 185. Large Scale Brain Networks. 6 units (2-0-4); third term. For course description, see Biology.
CNS/Bi/EE/CS/NB 186. Vision: From Computational Theory to Neuronal Mechanisms. 12 units (4-4-4); second term. Lecture, laboratory, and project course aimed at understanding visual information processing, in both machines and the mammalian visual system. The course will emphasize an interdisciplinary approach aimed at understanding vision at several levels: computational theory, algorithms, psychophysics, and hardware (i.e., neuroanatomy and neurophysiology of the mammalian visual system). The course will focus on early vision processes, in particular motion analysis, binocular stereo, brightness, color and texture analysis, visual attention and boundary detection. Students will be required to hand in approximately three homework assignments as well as complete one project integrating aspects of mathematical analysis, modeling, physiology, psychophysics, and engineering. Given in alternate years; Offered 2017–18. Instructors: Meister, Perona, Shimojo.
CNS/Bi/Ph/CS/NB 187. Neural Computation. 9 units (3-0-6); first term. Prerequisites: familiarity with digital circuits, probability theory, linear algebra, and differential equations. Programming will be required. This course investigates computation by neurons. Of primary concern are models of neural computation and their neurological substrate, as well as the physics of collective computation. Thus, neurobiology is used as a motivating factor to introduce the relevant algorithms. Topics include rate-code neural networks, their differential equations, and equivalent circuits; stochastic models and their energy functions; associative memory; supervised and unsupervised learning; development; spike-based computing; single-cell computation; error and noise tolerance. Instructor: Perona.
BE/CS/CNS/Bi 191 ab. Biomolecular Computation. 9 units (3-0-6). For course description, see Bioengineering.
Bi/CNS/NB 195. Mathematics in Biology. 9 units (3-0-6). For course description, see Biology.
Bi/CNS/NB 216. Behavior of Mammals. 6 units (2-0-4). For course description, see Biology.
Bi/CNS/NB 217. Central Mechanisms in Perception. 6 units (2-0-4). For course description, see Biology.
Bi/CNS/NB 220. Genetic Dissection of Neural Circuit Function. 6 units (2-0-4). For course description, see Biology.
Bi/CNS/BE/NB 230. Optogenetic and CLARITY Methods in Experimental Neuroscience. 9 units (3-2-4); third term. For course description, see Biology.
CNS/Bi/NB 247. Cerebral Cortex. 6 units (2-0-4); second term. Prerequisite: Bi/CNS/NB 150 or equivalent. A general survey of the structure and function of the cerebral cortex. Topics include cortical anatomy, functional localization, and newer computational approaches to understanding cortical processing operations. Motor cortex, sensory cortex (visual, auditory, and somatosensory cortex), association cortex, and limbic cortex. Emphasis is on using animal models to understand human cortical function and includes correlations between animal studies and human neuropsychological and functional imaging literature. Instructor: Andersen. Given in alternate years. Not Offered 2017–18.
Bi/CNS 250 c. Topics in Systems Neuroscience. 9 units (3-0-6). For course description, see Biology.
CNS/SS 251. Human Brain Mapping: Theory and Practice. 9 units (2-1-6); second term. A course in functional brain imaging. An overview of contemporary brain imaging techniques, usefulness of brain imaging compared to other techniques available to the modern neuroscientist. Review of what is known about the physical and biological bases of the signals being measured. Design and implementation of a brain imaging experiment and analysis of data (with a particular emphasis on fMRI). Instructor: O’Doherty.
SS/Psy/Bi/CNS 255. Topics in Emotion and Social Cognition. 9 units (3-0-6). For course description, see Social Science.
CNS/Bi/NB 256. Decision Making. 6 units (2-0-4); third term. This special topics course will examine the neural mechanisms of reward, decision making, and reward-based learning. The course covers the anatomy and physiology of reward and action systems. Special emphasis will be placed on the representation of reward expectation; the interplay between reward, motivation, and attention; and the selection of actions. Links between concepts in economics and the neural mechanisms of decision making will be explored. Data from animal and human studies collected using behavioral, neurophysiological, and functional magnetic resonance techniques will be reviewed. Given in alternate years; Offered 2017–18. Instructor: Andersen
CNS 280. Research in Computation and Neural Systems. Hours and units by arrangement. For graduate students admitted to candidacy in computation and neural systems.
SS/Psy/CNS 285. Topics in Social, Cognitive, and Decision Sciences. 3 units (3-0-0); first, second, third terms. For course description, see Social Sciences.
CNS/Bi 286 abc. Special Topics in Computation and Neural Systems. Units to be arranged. First, second, third terms. Students may register with permission of the responsible faculty member.