# Computation & Neural Sys(E&AS) (CNS) Undergraduate Courses (2020-21)

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: Siapas.

CNS/Psy/Bi 102 ab.
Brains, Minds, and Society.
9 units (3-0-6):
second, third terms.
Prerequisites: Bi/CNS/NB/Psy 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. Part a: Reinforcement learning. Unconscious and conscious processing. Emotion. Behavioral economics. Goal-directed and habit learning. Facial processing in social neuroscience. Part b: History and mechanisms of reinforcement. Associative learning. Mentalizing and strategic thinking. Neural basis of prosociality. Exploration-exploitation tradeoff. Functions of basal ganglia.
Instructors: O'Doherty/Adolphs, O'Doherty.

Psy/CNS 105 ab.
Frontiers in Neuroeconomics.
5 units (1.5-0-3.5):
second term.
The new discipline of Neuroeconomics seeks to understand the mechanisms underlying human choice behavior, born out of a confluence of approaches derived from Psychology, Neuroscience and Economics. This seminar will consider a variety of emerging themes in this new field. Some of the topics we will address include the neural bases of reward and motivation, the neural representation of utility and risk, neural systems for inter-temporal choice, goals vs habits, and strategic interactions. We will also spend time evaluating various forms of computational and theoretical models that underpin the field such as reinforcement-learning, Bayesian models and race to barrier models. Each week we will focus on key papers and/or book chapters illustrating the relevant concepts. Not offered 2020-21.

Psy/CNS 130.
Introduction to Human Memory.
9 units (3-0-6):
second term.
The course offers an overview of experimental findings and theoretical issues in the study of human memory. Topics include iconic and echoic memory, working memory, spatial memory, implicit learning and memory; forgetting: facts vs. skills, memory for faces; retrieval: recall vs. recognition, context-dependent memory, semantic memory, spreading activation models and connectionist networks, memory and emotion, infantile amnesia, memory development, and amnesia. Not offered 2020-21.

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 2020-21.
Instructor: O'Doherty.

Psy/CNS 132.
Computational Reinforcement-learning in Biological and Non-biological Systems.
9 units (3-0-6):
third term.
Reinforcement-learning concerns the computational principles by which animals and artificial agents can learn to select actions in their environment in order to maximize their future rewards. Over the past 50 years there has been a rich interplay between the development and application of reinforcement-learning models in artificial intelligence, and the investigation of reinforcement-learning in biological systems, including humans. This course will review this rich literature, covering the psychology of animal-learning, the neurobiology of reward and reinforcement, and the theoretical basis and application of reinforcement-learning models to biological and non-biological systems. Not offered 2020-21.

EE/CNS/CS 148.
Selected Topics in Computational Vision.
9 units (3-0-6):
third term.
Prerequisites: undergraduate calculus, linear algebra, geometry, statistics, computer programming.
The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D reconstruction. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system.
Instructor: Perona.

Bi/CNS/NB/Psy 150.
Introduction to Neuroscience.
10 units (4-0-6):
third term.
Prerequisites: Bi 8, 9, or instructor's permission.
General principles of the function and organization of nervous systems, providing both an overview of the subject and a foundation for advanced courses. Topics include the physical and chemical bases for action potentials, synaptic transmission, and sensory transduction; anatomy; development; sensory and motor pathways; memory and learning at the molecular, cellular, and systems level; and the neuroscience of brain diseases. Letter grades only.
Instructors: Adolphs, Lester.

Bi/CNS/NB 152.
Neural Circuits and Physiology of Appetite and Body Homeostasis.
6 units (2-0-4):
third term.
Prerequisites: Graduate standing or Bi/CNS/NB/Psy 150, or equivalent.
An advanced course of lectures, readings, and student presentations focusing on neural basis of appetites such as hunger and thirst. This course will cover the mechanisms that control appetites both at peripheral and central level. These include genetics, neural manipulation, and viral tracing tools with particular emphasis on the logic of how the body and the brain cooperate to maintain homeostasis. Given in alternate years; offered 2020-21.
Instructor: Oka.

Bi/CNS/NB 154.
Principles of Neuroscience.
9 units (3-0-6):
first term.
Prerequisites: Bi/CNS/NB/Psy 150 or equivalent.
This course aims to distill the fundamental tenets of brain science, unlike the voluminous textbook with a similar title. What are the essential facts and ways of understanding in this discipline? How does neuroscience connect to other parts of life science, physics, and mathematics? Lectures and guided reading will touch on a broad range of phenomena from evolution, development, biophysics, computation, behavior, and psychology. Students will benefit from prior exposure to at least some of these domains. Given in alternate years; offered 2020-21.
Instructor: Meister.

CMS/CS/CNS/EE/IDS 155.
Machine Learning & Data Mining.
12 units (3-3-6):
second term.
Prerequisites: CS/CNS/EE 156 a.
Having a sufficient background in algorithms, linear algebra, calculus, probability, and statistics, is highly recommended. This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. We will also discuss recent research developments.
Instructor: Pachter.

CS/CNS/EE 156 ab.
Learning Systems.
9 units (3-1-5):
first, third terms.
Prerequisites: Ma 2 and CS 2, or equivalent.
Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks.
Instructor: Abu-Mostafa.

Bi/CNS/NB 157.
Comparative Nervous Systems.
9 units (2-3-4):
third term.
Prerequisites: instructor's permission.
An introduction to the comparative study of the gross and microscopic structure of nervous systems. Emphasis on the vertebrate nervous system; also, the highly developed central nervous systems found in arthropods and cephalopods. Variation in nervous system structure with function and with behavioral and ecological specializations and the evolution of the vertebrate brain. Letter grades only. Given in alternate years; offered 2020-21.
Instructor: Allman.

Bi/CNS 158.
Vertebrate Evolution.
9 units (3-0-6):
third term.
Prerequisites: Bi 1, Bi 8, or instructor's permission.
An integrative approach to the study of vertebrate evolution combining comparative anatomical, behavioral, embryological, genetic, paleontological, and physiological findings. Special emphasis will be given to: (1) the modification of developmental programs in evolution; (2) homeostatic systems for temperature regulation; (3) changes in the life cycle governing longevity and death; (4) the evolution of brain and behavior. Letter grades only. Given in alternate years; not offered 2020-21.
Instructor: Allman.

CS/CNS/EE/IDS 159.
Advanced Topics in Machine Learning.
9 units (3-0-6):
third term.
Prerequisites: CS 155; strong background in statistics, probability theory, algorithms, and linear algebra; background in optimization is a plus as well.
This course focuses on current topics in machine learning research. This is a paper reading course, and students are expected to understand material directly from research articles. Students are also expected to present in class, and to do a final project. Not offered 2020-21.

Pl/CNS/NB/Bi 161.
Consciousness.
9 units (3-0-6):
second term.
Prerequisites: None, but strongly suggest prior background in philosophy of mind and basic neurobiology (such as Bi 150).
One of the last great challenges to our understanding of the world concerns conscious experience. What exactly is it? How is it caused or constituted? And how does it connect with the rest of our science? This course will cover philosophy of mind, cognitive psychology, and cognitive neuroscience in a mixture of lectures and in-class discussion. There are no formal pre-requisites, but background in philosophy (equivalent to Pl 41, Pl 110) and in neuroscience (equivalent to BI/CNS 150) is strongly recommended and students with such background will be preferentially considered. Limited to 20.
Instructors: Adolphs, Eberhardt.

Bi/CNS/NB 162.
Cellular and Systems Neuroscience Laboratory.
12 units (2-4-6):
second term.
Prerequisites: Bi/CNS/NB/Psy 150 or instructor's permission.
A laboratory-based introduction to experimental methods used for electrophysiological studies of the central nervous system. Through the term, students investigate the physiological response properties of neurons in vertebrate and invertebrate brains, using extra- and intracellular recording techniques. Students are instructed in all aspects of experimental procedures, including proper surgical techniques, electrode fabrication, and data analysis. The class also includes a brain dissection and independent student projects that utilize modern digital neuroscience resources. Not offered 2020-21.
Instructor: Bremner.

NB/Bi/CNS 163.
The Biological Basis of Neural Disorders.
6 units (3-0-3):
second term.
Prerequisites: Bi/CNS/NB/Psy 150 or instructor's permission.
The neuroscience of psychiatric, neurological, and neurodegenerative disorders and of substance abuse, in humans and in animal models. Students master the biological principles including genetics, cell biology, biochemistry, physiology, and circuits. Topics are taught at the research level and include classical and emerging therapeutic approaches and diagnostic strategies. Given in alternate years; Not offered 2020-21.
Instructors: Lester, Lois.

Bi/CNS/NB 164.
Tools of Neurobiology.
9 units (3-0-6):
first term.
Prerequisites: Bi/CNS/NB/Psy 150 or equivalent.
Offers a broad survey of methods and approaches to understanding in modern neurobiology. The focus is on understanding the tools of the discipline, and their use will be illustrated with current research results. Topics include: molecular genetics, disease models, transgenic and knock-in technology, virus tools, tracing methods, gene profiling, light and electron microscopy, optogenetics, optical and electrical recording, neural coding, quantitative behavior, modeling and theory.
Instructor: Meister.

CS/CNS/EE/IDS 165.
Foundations of Machine Learning and Statistical Inference.
12 units (3-3-6):
second term.
Prerequisites: CMS/ACM/IDS 113, ACM/EE/IDS 116, CS 156 a, ACM/CS/IDS 157 or instructor's permission.
The course assumes students are comfortable with analysis, probability, statistics, and basic programming. This course will cover core concepts in machine learning and statistical inference. The ML concepts covered are spectral methods (matrices and tensors), non-convex optimization, probabilistic models, neural networks, representation theory, and generalization. In statistical inference, the topics covered are detection and estimation, sufficient statistics, Cramer-Rao bounds, Rao-Blackwell theory, variational inference, and multiple testing. In addition to covering the core concepts, the course encourages students to ask critical questions such as: How relevant is theory in the age of deep learning? What are the outstanding open problems? Assignments will include exploring failure modes of popular algorithms, in addition to traditional problem-solving type questions.
Instructor: Anandkumar.

CS/CNS 171.
Computer Graphics Laboratory.
12 units (3-6-3):
first term.
Prerequisites: Extensive programming experience and proficiency in linear algebra, starting with CS 2 and Ma 1 b.
This is a challenging course that introduces the basic ideas behind computer graphics and some of its fundamental algorithms. Topics include graphics input and output, the graphics pipeline, sampling and image manipulation, three-dimensional transformations and interactive modeling, basics of physically based modeling and animation, simple shading models and their hardware implementation, and some of the fundamental algorithms of scientific visualization. Students will be required to perform significant implementations.
Instructor: Barr.

CS/CNS 174.
Computer Graphics Projects.
12 units (3-6-3):
third term.
Prerequisites: Extensive programming experience, CS/CNS 171 or instructor's permission.
This laboratory class offers students an opportunity for independent work including recent computer graphics research. In coordination with the instructor, students select a computer graphics modeling, rendering, interaction, or related algorithm and implement it. Students are required to present their work in class and discuss the results of their implementation and possible improvements to the basic methods. May be repeated for credit with instructor's permission.
Instructor: Barr.

CNS/Bi/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. Given in alternate years; Offered 2020-21.
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):
third term.
This class focuses on the primate visual system, investigating it from an experimental, psychophysical, and computational perspective. The course will focus on two essential problems: 3-D vision and object recognition. We will examine how a visual stimulus is represented starting in the retina, and ending in the frontal lobe, with a special emphasis placed on mechanisms for high-level vision in the parietal and temporal lobes. An important aspect of the course is the lab component in which students design and analyze their own fMRI experiment. Given in alternate years; not offered 2020-21.
Instructor: Tsao.

Bi/CNS/NB 185.
Large Scale Brain Networks.
6 units (2-0-4):
third term.
This class will focus on understanding what is known about the large-scale organization of the brain, focusing on the mammalian brain. What large scale brain networks exist and what are their principles of function? How is information flexibly routed from one area to another? What is the function of thalamocortical loops? We will examine large scale networks revealed by anatomical tracing, functional connectivity studies, and mRNA expression analyses, and explore the brain circuits mediating complex behaviors such as attention, memory, sleep, multisensory integration, decision making, and object vision. While each of these topics could cover an entire course in itself, our focus will be on understanding the master plan-how the components of each of these systems are put together and function as a whole. A key question we will delve into, from both a biological and a theoretical perspective, is: how is information flexibly routed from one brain area to another? We will discuss the communication through coherence hypothesis, small world networks, and sparse coding. Given in alternate years, not offered 2020-21.
Instructor: Tsao.

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; Not Offered 2020-21.
Instructors: Meister, Perona, Shimojo, Tsao.

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. Not Offered 2020-21.
Instructor: Perona.

BE/CS/CNS/Bi 191 ab.
Biomolecular Computation.
9 units (3-0-6) second term; (2-4-3) third term:
second, third terms.
Prerequisites: none. Recommended: ChE/BE 163, CS 21, CS 129 ab, or equivalent.
This course investigates computation by molecular systems, emphasizing models of computation based on the underlying physics, chemistry, and organization of biological cells. We will explore programmability, complexity, simulation of, and reasoning about abstract models of chemical reaction networks, molecular folding, molecular self-assembly, and molecular motors, with an emphasis on universal architectures for computation, control, and construction within molecular systems. If time permits, we will also discuss biological example systems such as signal transduction, genetic regulatory networks, and the cytoskeleton.
Instructor: Winfree.

Bi/CNS/NB 195.
Mathematics in Biology.
9 units (3-0-6):
first term.
Prerequisites: calculus.
This course develops the mathematical methods needed for a quantitative understanding of biological phenomena, including data analysis, formulation of simple models, and the framing of quantitative questions. Topics include: probability and stochastic processes, linear algebra and transforms, dynamical systems, scientific programming.
Instructor: Thomson.

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