The MBC/IGERT Graduate Training Program

One of the main goals of the Center for Mind, Brain, and Computation is to train future scientists to investigate the emergent functions of neural systems in the brain using a combination of computational and experimental approaches and/or to investigate the computational properties of brain-like processes and mechanisms. This goal is addressed by the MBC/IGERT graduate training program.. The program is supported by an NSF Integrative Graduate Education and Research Training (IGERT) Grant.

Our program is grounded in the idea that contemporary research on the emergent functions of the nervous system often depends on the synergistic integration of experimental and computational methods. In seeking to train students to integrate these methods, a flexible, learn-through-experience approach is needed. The program is built around a flexible program of individually-chosen courses and independent study that will allow each student to gain just the right background for their needs, followed by the MBC Research Experience, which generally will exploit the specific background acquired through the training. The program also seeks to provide a supportive overall training environment through course offerings, seminars, and other events that will help foster fuller integration of computational/quantitative and experimental approaches at many levels of investigation in neuroscience.

The program seeks especially to train students who wish to make a serious commitment to extend their research training and research skill set in the direction of achieving an integration of approaches. Two levels of participants in the program are recognized: Trainees and Affiliates.

Trainees are Ph. D. students who commit to a specialized training program, including individually selected course work and an integrative research project, as discussed below. Trainees who are US nationals are eligible to be considered for two years of stipend and partial tuition support funded by the IGERT grant. Affiliates are Ph.D. students or other members of the Stanford community who are interested in the research themes and approach of MBC, but who are either (a) preparing to become trainees or (b) benefiting from the activities of the program without making the full commitment required of trainees. For information on Joining MBC either as an affiliate or as a trainee, see Join MBC.

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Program Structure

Prospective trainees create an individualized training program, comprising a series of courses that provide a strong grounding in a new research method complementing the student's primary Ph. D. training. Integrative educational experiences, including the MBC Research Experience, accompany that specialized training. The MBC Research Experience allows students to utilize their new knowledge under the joint supervision of a primary home-department mentor and a secondary mentor with appropriately balanced expertise.

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Other program activities

The MBC offers an ongoing bi-weekly seminar series devoted to faculty and student research presentations and coordinated by advanced program students working with a member of the training faculty.  The MBC also holds an annual workshop that brings distinguished speakers to campus to talk with program participants.  Trainees play an important role in formulating, planning, and coordinating the workshop along with program faculty.

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Individualized Training Plan and Mentoring

Each trainee is expected to formulate an individualized training and research plan (ITP). One goal of this plan is to prepare the student for the MBC research experience that complements or extends the training provided by the primary research department and mentor.

The ITP will generally involve several courses, three of which should go beyond the requirements of the student's home department. For example, a student admitted to the Neuroscience Graduate Program (NGP) might take one computer science course, one robotics course, and one psychology course (outside NGP requirements), and might select two computational neuroscience courses from available NGP program options (including new course offerings as described below, which will be included as NGP options).   Individual study guided by a faculty mentor can be proposed in lieu of formal coursework.

The program leadership will work with each student and the student's primary advisor to identify a secondary faculty mentor to play a key role in the student's education and research training.

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MBC Research Experience

A central element of the Stanford MBC program is the MBC Research Experience, in which students integrate both computational and experimental expertise through research supervised by both mentors.

The exact structure of the MBC research experience will vary from case to case. A student might conduct a multiple-quarter research project in the secondary mentor's laboratory, acquiring expertise in a method not available in the primary mentor's lab. Alternatively, the student might also divide time between the two labs for an extended period, or import a method from the secondary lab into activities within the primary laboratory, under ongoing joint supervision of both mentors. For further details, see Information for Potential Trainees.

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Relevant Courses

This list is updated frequently as course offerings change.

NBIO 206. The Nervous System

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. 8 units, J. Raymond..

Offered Winter 2015-2016, TH 2:30-3:30PM; Lab Th 3:30-5:00PM.


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. 5 units, L. Giocomo, E. Knudsen.

Offered 2016-2017.


NBIO 258. Information and Signaling Mechanisms in Neurons and Circuits (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. 4 units, S. Baccus.

Offered Fall 2015-2016, MW 12:30-2:20PM, Li Ka Shing Center, Rooms 205/206.

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PSYCH 209. Neural Network and Deep Learning Models for Cognition and Cognitive Neuroscience

Models of cognitive and developmental processes and the brain basis of such processes, including perception, attention, memory, decision making, language processing, acting and thinking. Models considered will include neural network models including contemporary deep learning models, as well as other process models spanning a spectrum from abstract to neurally realistic. Relationships between such models and more abstract models of cognitive processes including probabilistic models will be explored. 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. For advanced undergraduates and graduate students. Recommended: some familiarity with computer programming, differential equations, linear algebra, and/or probability theory, and courses in cognitive psychology and/or cognitive neuroscience.
4 units, J. McClelland.

Offered 2014-2015.

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CS 228. Probabilistic Graphical Models: Principles and Techniques

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. 3-4 units, S. Ermon.

Offered Winter 2015-2016, TTH 9:00-10:20AM, Skillaud Building.

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CS 229. Machine Learning

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. 3-4 units, J. Duchi and A. Ng.

Offered Fall 2015-2015 A. Ng, MW 9:30-10:50AM, Huang Engineering Center, NVIDIA Auditorium. Offered Spring 2015-2016 J. Duchi, MWF 1:30-2:50PM, Huang Engineering Center, NVIDIA Auditorium.

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CS 379C. Computational Models of the Neocortex

In collaboration with the Allen Institute for Brain Science (AIBS), HHMI Janelia Farm, Max Planck Institute for Medical Research and MIT, we are compiling EM (Electron Microscopy) datasets that will enable computer scientists to reconstruct the neural circuits for several model organisms, and co-registered activity recordings using calcium imaging (CI) from which we hope to glean algorithmic insights by fitting various artificial neural network models to account for observed input / output behavior.

We’ll be working with two teams of scientists and engineers who are building the tools to acquire this data. We have several relatively-small (10TB) EM datasets (including ground truth) that students interested in circuit tracing (structural connectomics) can use in projects. Scientists at AIBS have volunteered to help students in understanding the data and technologies used to collect it. In addition, engineers from my team at Google will supply examples of algorithms that have worked well for us.

Inferring function from CI data is more challenging since until recently there haven’t been good datasets to work with. We now have several such datasets provided by our collaborators that can be used in student projects. In addition, we’ll be generating synthetic datasets for cortical circuits of 5-50K neurons using Hodgkin-Huxley models (1) developed at AIBS and EPFL. These models and their associated simulators provide a controlled environment in which to experiment with and evaluate machine-learning technologies for functional connectomics.

The prerequisites are basic high-school biology, good math skills, and familiarity with machine learning. Some background in computer vision and signal processing will be important for projects in structural connectomics. Familiarity with modern artificial neural network technologies is a plus for projects in functional connectomics. Please encourage your qualified students to consider taking the course. As an added incentive, I have a group of extraordinary scientists and engineers lined up to help make it a great course.

(1) These models developed by Costas Anastassiou and his team at AIBS and Sean Hill at EPFL consist of networks of reconstructed, multi-compartmental, virtually-instrumented and spiking pyramidal neurons and basket cells, plus ion- and voltage-dependent currents and local field potentials that allow us to generate the same sort of rasters we expect to collect during calcium imaging. 3 units, T. Dean.

Offered 2014-2015.

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NENS 220. Computational Neuroscience

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.
4 units, J. Huguenard.

Offered Spring 2015-2016.

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APPPHYS 205//BIO 126/226. Introduction to Biophysics

Core course appropriate for advanced undergraduate students and graduate students with prior knowledge of calculus and a college physics course. Introduction to how physical principles offer insights into modern biology, with regard to the structural, dynamical, and functional organization of biological systems. Topics include the roles of free energy, diffusion, electromotive forces, non-equilibrium dynamics, and information in fundamental biological processes. 3-4 units, S. Ganguli and M. Schnitzer.

Offered Winter 2015-2016, TTH 1:30-2:50PM.


APPPHYS 223. Stochastic and Nonlinear Dynamics

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. 3 units, D. Fisher.

Offered 2013-2014.

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APPPHYS 293. Theoretical Neuroscience

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.
3 units, S. Ganguli.

Offered Spring 2015-2016, TTH 1:30PM-2:50PM, Location TBA.


BioE 332. Large-Scale Neural Modeling

This course examines the dynamics of large networks of spiking neurons (several thousand), with particular focus on how these networks achieve cognitive behaviors such as working memory, selective attention, and decision making. The course will feature lectures and labs using two Python-based simulators: Brian, a software platform, and Neurogrid, a hardware platform that simulates up to a million spiking neurons in real time. Most of the course will be project-based, allowing students to explore their individual interests. 3 units, K. Boahen.

Offered Spring 2015-2016, WF 1:30-2:50PM, Location TBA.


MCP 222. Imaging: Biological Light Microscopy (BIO 152)

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. 3 units,R. Lewis..

Offered Spring 2015-2016, TTH 1:30-3:20PM, Li Ka Shing Center, Room 306.

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Psych 13S. Dynamical models of mental processes: Development, analysis, and simulation

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.

Note: Dr. Gao is a Research Associate in the Psychology Department at Stanford.   She received her PhD in Mechanical and Aerospace Engineering at Princeton University, and has about ten years of modeling experience in cognitive psychology and computational neuroscience. 2 units, J. Gao.

Offered Summer 2011-2012.


Psych 204A. Human Neuroimaging Methods

An introduction to human neuroimaging using magnetic resonance. The course is a mixture of lectures and hands-on software tutorials. The course begins by introducing basic MR principles. Then various MR measurement modalities are described, including several types of structural and functional imaging methods. Finally algorithms for analyzing and visualizing the various types of neuroimaging data are explained, including anatomical images, functional data, diffusion imaging (e.g., DTI) and magnetization transfer. Emphasis is on explaining software methods used for interpreting these types of data. 3 units, B. Wandell.

Offered Winter 2015-2016, TTH 1:30-2:50PM, Bldg 260, Room 003.


Psych 204B. Computational Neuroimaging: Analysis Methods

Neuroimaging methods with focus on data analysis techniques. Basic MR physics and BOLD signals. Methods for neuroimaging data using real and simulated data sets. Topics include: linearity of the fmri signal; time versus space resolution tradeoffs; noise in neuroimaging; correlation analysis; visualization methods; cortical reconstruction, inflation, and flattening; reverse engineering; can cognitive states be predicted from brain activation? Prerequisite: consent of instructor. 1-3 units, K. Grill-Spector.

Offered Spring 2015-2016, TTH 9:00-10:20AM, Jordan Hall, Bldg 420, Room 419.


Psych 204. Computation and Cognition: the Probabilistic Approach

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. 3-4 units, N. Goodman.

Offered Spring 2014-2015.

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Psych 209a. The Neural Basis of Cognition: A Parallel Distributed Processing Approach

Models and data to support the notion that brain representations are patterns of activity over widely dispersed populations of neurons, that mental processing involves coherent distributed engagement of neurons in these populations, and that learning and development occur primarily through the adjustment of the strengths of the connections between the neurons. How models may be used to explain aspects of human cognition, development, and effects of brain damage on cognition. Prerequisites: linear algebra, differential equations, a programming course, and two courses in psychology or neuroscience. J. McClelland.

Offered Winter 2011-2012.


Psych 303. Human and Machine Hearing

Overview of key problems in human hearing; linear and nonlinear system theory applied to sound and hearing; understanding how to model human hearing in the form of algorithms that can process general sounds efficiently; how to construct, display, and interpret "auditory images"; how to extract features compatible with machine-learning systems; how to build systems that extract information from sound to do a job; and example applications of machine hearing to speech, music, security and surveillance, personal sound diaries, smart house, etc. Prerequisites: basic calculus and algorithms. 3 units, R. F. Lyon. (Richard Lyon, research scientist, Google, Inc.)

Offered Fall 2010-2011.


RAD 227. Functional MRI Methods (BIOPHYS 227//BioE 227)

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. 3 units, G. Glover.

Offered Winter 2015-2016, TTH 9:00-10:20AM, Lucas Center.


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Courses in Visualization of the Nervous System

Over the last several years there has been dramatic growth and development of methods for visualization in the nervous system, and in concert with this two whole year-long sequences of courses have arisen at Stanford, addressing very different levels of analysis.

At the macroscopic level, a sequence of three courses is taught by Professors Brian Wandell ( Psychology), Gary Glover (Director of the Lucas Center for Imaging), and Kalanit Grill-Spector (Professor, Psychology). This sequence describes the physics of magnetic resonance, different ways to control magnetic resonance imagers to measure chemical properties, diffusion, and functional activity; methods for measuring animal and human brain structure and activity using magnetic resonance; experimental designs, statistical and signal processing methods; and modeling from cellular signals to BOLD. This sequence introduces our students to the relationship between measurements at the level of functional magnetic resonance imaging (fMRI) and cellular signals.

A parallel sequence of courses, addressing visualization at a micro level, is taught by Professors Stephen Block (Biological Sciences), Mark Schnitzer (Applied Physics and Biological Sciences) and Stephen Smith (Molecular and Cellular Physiology).  In this course students learn a variety of imaging methods that span multiple length scales. This two-course sequence explains microscope optics, resolution limits, single-molecule fluorescence, FRET, confocal microscopy, two-photon microscopy, and optical trapping.

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Training Program Steering Committee


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