Courses

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  • Fall

    • CS 229. Machine Learning
      Instructor: A. Ng
      Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.

      MW 9:30AM - 10:45AM -- NVIDIA Auditorium
      3-4 units
      Computer Science Department
      Offered: Fall
    • NBIO 258. Information and Signaling Mechanisms in Neurons and Circuits
      (Same as MCP 258.) How synapses, cells, and neural circuits process information relevant to a behaving organism. How phenomena of information processing emerge at several levels of complexity in the nervous system, including sensory transduction in molecular cascades, information transmission through axons and synapses, plasticity and feedback in recurrent circuits, and encoding of sensory stimuli in neural circuits.

      Please check Axess for time and location.
      4 units
      Neurobiology Department
      Offered: Fall
      This course is not offered this year.
    • PSYCH 204. Computation and cognition: the probabilistic approach
      Instructor: N. Goodman
      This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.

      TTh 1:30-3:00 -- Bldg 50, Room 52H
      3-4 units
      Psychology Department
      Offered: Fall
  • Winter

    • APPHYS 205//BIO 126/226. Introduction to Biophysics
      Instructor: S. Ganguil and M. Schnitzer
      Part I: How should we understand the electrical function of nerves? How do we describe the electrical activity of the neural networks that perform spectacular tasks in our everyday lives? This course seeks to address such fundamental questions about nervous activity. Part II: How do we quantitatively understand fundamental biological processes occurring at a sub-cellular level (i.e. nanometer scale) through the application of basic physics? i.e. how does an understanding of nanometer scale physics elucidate the structural and function organization of biochemical networks, and how do they compute reliably in the presence of thermal fluctuations, which dominate at this scale? This course seeks to address such fundamental questions about both neuronal and biochemical computation. Course Goals: The overarching goal of the course is to teach students the biophysical basis for biological phenomena and to allow students to use computational methods and physical principles as predictive tools. A few fundamental physical principles will be seen to give rise to a rich set of dynamical activities. Quantitative approaches will be used to describe these physical principles and to create analytical and numerical models of neuronal dynamics. Another important goal is to convey the flavor and excitement of interdisciplinary biological science. The student audience is expected to be diverse, with representation from the Biology, Neuroscience, Biophysics, Bioengineering, Applied Physics, and other Biological Sciences programs. Students will be strongly encouraged to work together with class members from outside their home program, and to learn from others with complementary scientific backgrounds. Course structure and assignments will be designed to promote student-student interactions as well as experience with research literature readings and both computational and analytical analysis. Thus, both biological science students and physics/engineering students should find the course challenging, but for different reasons.

      MW 12:35-2:05 -- McCullough 122
      3-4 units
      Applied Physics and Biology Department
      Offered: Winter
    • NBIO 206. The Nervous System
      Instructor: T. Moore
      Structure and function of the nervous system, including neuroanatomy, neurophysiology, and systems neurobiology. Topics include the properties of neurons and the mechanisms and organization underlying higher functions. Framework for general work in neurology, neuropathology, clinical medicine, and for more advanced work in neurobiology. Lecture and lab components must be taken together.

      MF 9:00-10:50, Lab Th 1:15-5:05 -- Li Ka Shing Center, Room 130
      7-8 units
      Neurobiology Department
      Offered: Winter
    • PSYCH 209. Models of Cognitive Processes
      Instructor: J. McClelland
      For advanced undergraduates and graduate students. Models of cognitive and developmental processes, including perception, attention, memory, decision making, acting and thinking; and on modeling cognitive development, domain learning, and skill acquisition as processes that take place over time. Models considered will include parallel distributed processing models and other types of artificial neural network models as well as process models spanning a spectrum from abstract to neurally realistic. Students learn about classic models and carry out exercises in the first six weeks and will undertake projects and learn about recent developments during the last four weeks of the quarter. Recommended: computer programming ability, familiarity with differential equations, linear algebra, and probability theory, and courses in cognitive psychology and/or cognitive/systems neuroscience.

      MWF 11:00AM - 12:15PM -- Hewlett Teaching Center Room 101
      4 units
      Psychology Department
      Offered: Winter
    • RAD 227. Functional MRI Methods (BIOPHYS 227)
      Instructor: G. Glover
      Basics of functional magnetic resonance neuroimaging, including data acquisition, analysis, and experimental design. Journal club sections. Cognitive neuroscience and clinical applications. Prerequisites: basic physics, mathematics; neuroscience recommended.

      TTh 2:00-3:30 -- Lucas Center Conf Room, P083
      3 units
      Radiology Department
      Offered: Winter
  • Spring

    • APPPHYS 223. Stochastic and Nonlinear Dynamics (BIO 223)
      Instructor: D. Fisher
      Theoretical analysis of dynamical processes: dynamical systems, stochastic processes, and spatiotemporal dynamics. Motivations and applications from biology and physics. Emphasis is on methods including qualitative approaches, asymptotics, and multiple scale analysis. Prerequisites: ordinary and partial differential equations, complex analysis, and probability or statistical physics.

      MW 2:15-3:45 -- Sequoia Hall Room 200
      3 units
      Physics Department
      Offered: Spring
    • APPPHYS 293. Theoretical Neuroscience
      Instructor: S. Ganguli
      Introduction to fundamental theoretical ideas that provide conceptual insights into how networks of neurons cooperatively mediate important brain functions. Topics include basic mathematical models of single neurons, neuronal computation through feedforward and recurrent network dynamics, principles of associative memory, applications of information theory to early sensory systems, correlations and neural population coding, network plasticity and the self-organization of stimulus selectivity, and supervised and unsupervised learning through multiple mechanisms of synaptic plasticity. Emphasis on developing mathematical and computational skills to analyze complex neural systems. Prerequisites: calculus, linear algebra, and basic probability theory, or consent of instructor.

      TTh 9:00-10:45 -- TBA
      3 units
      Applied Physics Department
      Offered: Spring
    • BioE332. Large-Scale Neural Modeling
      Instructor: K. Boahen
      Large-scale models link cellular properties, columnar microcircuits, recurrent connectivity, and feedback projections to experimentally studied behaviors such as selective attention and working memory. Emphasis is on making experimentally testable predictions by exploring spike-based communication and biophysics-based computation. Work in teams of two to implement models from the literature and develop models of your own. Run models with up to a million neurons in real-time on a special-purpose simulation platform developed at Stanford (Neurogrid).

      WF 12:50-2:05 -- TBA
      3 units
      Bioengineering Department
      Offered: Spring
    • CS 228. Probabilistic Graphical Models: Principles and Techniques
      Instructor: D. Koller
      Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.

      MW 3:15pm-5:05pm -- TBA
      3-4 units
      Computer Science Department
      Offered: Spring
    • CS 379C. Computational Models of the Neocortex
      Instructor: T. Dean
      Reprisal of course offered spring 2012 of the same name ; see http://www.stanford.edu/class/cs379c/ for more detail ; which emphasized scaling the technologies of systems neuroscience to take advantage of the exponential trend in computational power known as Moore's Law. Course covers many of the same topics but will focus on the near-term prospects for practical advances in health care, prosthetic augmentation, and artificial intelligence inspired by biological systems. Graded pass / no credit on the basis of class participation, a midterm white paper or business prospectus and a final technical report evaluating an appropriate technology selected in collaboration with the instructor. Focus will be on examining the assumptions underlying current claims for realizing the potential benefits of research in neuroscience and identifying real business opportunities, disruptive new technologies and advances in medicine that could substantially benefit patients within the next decade. Technology-minded critical thinkers seriously interested in placing their bets and picking careers in related areas of business, technology and science are welcome. Prerequisites: basic probability theory, algorithms, and statistics.

      MW 4:15-5:30 -- Gates 100
      3 units
      Computer Science Department
      Offered: Spring
    • MCP 222. Imaging: Biological Light Microscopy (BIO 152)
      Instructor: S.J. Smith and R.S. Lewis
      Survey of instruments which use light and other radiation for analysis of cells in biological and medical research. Topics: basic light microscopy through confocal fluorescence and video/digital image processing. Lectures on physical principles; involves partial assembly and extensive use of lab instruments. Lab. Prerequisites: some college physics, Biology core.

      Please check Axess for time and location.
      3 units
      Molecular and Cellular Physiology Department
      Offered: Spring
      This course is not offered this year.
    • NBIO 218. Neural Basis of Behavior
      Advanced seminar. The principles of information processing in the nervous system and the relationship of functional properties of neural systems with perception, behavior, and learning. Original papers; student presentations. Prerequisite: NBIO 206 or consent of instructor.

      Please check Axess for time and location.
      5 units
      Bio-X Department
      Offered: Spring
      This course is not offered this year.
    • NENS 220. Computational Neuroscience
      Instructor: J. Huguenard
      Computational approaches to neuroscience applied at levels ranging from neurons to networks. Addresses two central questions of neural computation: How do neurons compute; and how do networks of neurons encode/decode and store information? Focus is on biophysical (Hodgkin-Huxley) models of neurons and circuits, with emphasis on application of commonly available modeling tools (NEURON, MATLAB) to issues of neuronal and network excitability. Issues relevant to neural encoding and decoding, information theory, plasticity, and learning. Fundamental concepts of neuronal computation; discussion focus is on relevant literature examples of proper application of these techniques. Final project. Recommended for Neuroscience Program graduate students; open to graduate, medical, and advanced undergraduate students with consent of instructor. Prerequisite: NBIO 206. Recommended: facility with linear algebra and calculus.

      Please check Axess for time and location.
      4 units
      Neurology & Neurological Sciences Department
      Offered: Spring
      This course is not offered this year.
  • Summer

    • PSYCH 13S. Dynamical models of mental processes: Development, analysis, and simulation
      Instructor: J. Gao
      Mathematical modeling has been a critical component in modern psychological and cognitive neuroscience research on the dynamics of mental processes. This course is designed to equip the new generation of such scientists with tailored mathematical knowledge to develop models of their own. I will use classical models and my own experience in modeling decision making as examples to demonstrate the process from vague ideas to the development, refinement, analysis and simulation of dynamical models. Along the way, systematic knowledge in differential equations, numerical methods, principle component analysis etc will be provided to facilitate the general ground for future models of students choosing. Open to graduate students and advanced undergraduates.

      Please check Axess for time and location.
      2 units
      Psychology Department
      Offered: Summer