The MBC Graduate Training Program

The Center for Mind, Brain and Computationís graduate training program is grounded in the idea that contemporary research on the emergent functions of the nervous system often depends on the integration of experimental and computational methods. One of the main goals of the training program is to support future scientists as they stretch beyond the traditional boundaries of their department or lab to learn a combination of empirical and computational approaches that they can integrate to advance their research.

The program is built around a flexible program of individually-chosen courses and independent study that will allow each student to gain the right background for their needs. 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.

For information on joining MBC as a graduate trainee, see Join MBC.

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Individualized Training and Research Plans

As part of their application to the MBC graduate training program, each applicant is required to formulate individualized training and research plans. The studentís training plan will generally involve several courses that go beyond the requirements of the student's home department, and provide strong grounding in a new research method complementing the student's primary PhD training. For example, a student admitted to the neuroscience graduate program might take a computer science course, a robotics course, and a psychology course (outside the neuroscience program requirements), and might select two computational neuroscience courses from available neuroscience program options. Individual study guided by a faculty mentor may be proposed in lieu of formal coursework. The most important criterion is that the student intends to stretch in a meaningful way that is recognizable to the programís faculty committee. Integrative educational experiences, including attendance at regular MBC seminars and travel to related conferences, build upon each studentís coursework.

In their application to the MBC training program, students also propose a research plan that explains how they will integrate empirical and quantitative or engineering methods to advance their area of study. Student trainees will apply their new knowledge to their research under the joint supervision of their primary mentor and secondary mentors.

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Each applicant will need to obtain approval of their training plan from both their primary research mentor and secondary mentor. The secondary mentor should provide expertise in a field outside the expertise of the studentís home lab or PhD program (e.g., in engineering or computer science if your primary lab's strength is in neurobiology), and support the student as they stretch to learn new approaches and apply them to their research.

Secondary mentors need not come from Stanford, and may be international. It is best if the primary and secondary mentors have a sense of mutual understanding of the student's overall training and research goals, and of the goals of the MBC graduate training program for the student's research and training. An established working relationship between the two mentors is beneficial, and will be especially important if the co-mentor works outside of Stanford.

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Other Program Activities

The MBC offers a journal club, an ongoing bi-weekly seminar series devoted to faculty and student research presentations, and also holds an annual symposium that brings distinguished speakers to campus to talk with program participants. Community engagement and participation is an important component of the MBC graduate training program, and trainees play an important role in formulating, planning, and coordinating these events along with program faculty.

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Program Support

All MBC graduate trainees in good standing are eligible for $1,500 to support research- and training-related travel each year for up to three years, and a one-time allocation of up to $3,000 to cover other research-related expenses.

To remain in good standing, trainees must submit an annual progress report that demonstrates their commitment to the training, research and engagement aspects of the MBC program, and that is approved by a member of the MBC graduate training program committee

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Relevant Courses

Please also check Stanford's official course listings, as course offerings change frequently.

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. 6 units, S. Baccus..

Offered Winter 2017-2018, MF 9:30-11:20AM, TH 1:30-3:20 and 3:30-4:50PM; Li Ka Shing Center, Room 130.


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, T. Moore.

Offered Spring 2016-2017, MTH 10:00-11:50AM; Li Ka Shing Center, Rooms 205/206.


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 2018-2019.

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PSYCH 209. Neural Network Models of Cognition: Principles and Applications

Neural Network models of cognitive and developmental processes and the neural basis of these processes, including contemporary deep learning models. Students learn about fundamental computational principles and classical as well as contemporary applications and carry out exercises in the first six weeks, then undertake projects during the last four weeks of the quarter. Recommended: computer programming ability, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognition, cognitive development or cognitive/systems neuroscience.
4 units, J. McClelland.

Offered Winter 2017-2018, TTH 10:30-11:50AM, Jordan Hall, Bldg 420, Room 417.

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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 2017-2018, TTH 9:00-10:20AM, Gates B1.

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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, D. Boneh and A. Ng.

Offered Fall 2017-2018, MW 9:30-10:50AM, Huang Engineering Center, NVIDIA Auditorium.

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CS 379C. Computational Models of the Neocortex

This course emphasizes approaches to scaling the technologies of computer science and systems neuroscience to take advantage of the exponential trend in computational power known as Moore's Law. Modern methods in signal processing and machine learning are combined with technologies for managing large datasets common in industry. Classes feature scientists presenting novel approaches for analyzing the structure and function of complex neural circuits. Grading is based on class participation (30%), a project proposal due at midterm (20%), and a final project demonstration and report due by the end of finals (50%). Team projects are encouraged, especially multi-disciplinary collaborations. Prerequisites are basic high-school biology, good math skills and familiarity with machine learning. Some background in computer vision and signal processing is important for projects in structural analysis. Familiarity with modern artificial neural network technologies is a plus for projects in functional analysis. For more detail, see with special attention to the CALENDAR and DISCUSSION tabs from past classes available by following the ARCHIVES link. This quarter, we consider the following three challenge problems:
1. How would you design a personal assistant capable of maintaining relationships with each member of a family, managing a comprehensive episodic memory to enrich those relationships, adopting a different intentional stance appropriate for each household member and essentially behaving as another member of the family?
2. What if you had all of the C++ — or Java or Python — code checked into GitHub plus all the versions and all the diffs plus sample I/O, unit tests and documentation. How would you go about developing a neural network architecture that learns to write programs from sample I/O and natural language descriptions?
3. Suppose you had the complete wiring diagram (connectome) of a fly and petabytes of recordings from each neuron in the fly's brain aligned with high-speed images recording every aspect of the fly's behavior and the environment in which those behaviors were carried out. How would you construct a model of the fly's brain?
Hypothesis: Each of these problems can be solved using a recurrent neural network architecture constructed from published component networks each of which is relatively well understood and has been applied successfully to solving simpler problems. 3 units, T. Dean.

Offered Spring 2017-2018, TTH 4:30-5:50PM, Hewlett Teaching Center, Room 101.

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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.

Not offered this year 2017-2018.


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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.

Offered Winter 2017-2018, TTH 1:30-2:50PM, Sloan Hall, Bldg 380, Room 380-F.


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 Spring 2018-2019.

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APPPHYS 293. Theoretical Neuroscience

Survey of advances in the theory of neural networks, mainly (but not solely) focused on results of relevance to theoretical neuroscience.Synthesizing a variety of recent advances that potentially constitute the outlines of a theory for understanding when a given neural network architecture will work well on various classes of modern recognition and classification tasks, both from a representational expressivity and a learning efficiency point of view. Discussion of results in the neurally-plausible approximation of back propagation, theory of spiking neural networks, the relationship between network and task dimensionality, and network state coarse-graining. Exploration of estimation theory for various typical methods of mapping neural network models to neuroscience data, surveying and analyzing recent approaches from both sensory and motor areas in a variety of species. Prerequisites: calculus, linear algebra, and basic probability theory, or consent of instructor. 3 units, S. Ganguli and D. Yamins.

Offered Spring 2017-2018, TTH 1:30PM-2:50PM, Hewlett Teaching Center, Room 200.


CS375. Large-Scale Neural Modeling for Neuroscience (PSYCH 249)

Introduction to designing, building, and training neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics, memory and attention; integration of variational and generative methods for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models, and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognitive or systems neuroscience.3 units, D. Yamins.

Offered Fall 2017-2018, WF 4:30-5:50PM, Lathrop 299.


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

This intensive laboratory and discussion course will provide participants with the theoretical and practical knowledge to utilize emerging imaging technologies based on light microscopy. Topics include microscope optics, resolution limits, Köhler illumination, confocal microscopy, fluorescence, two-photon, TIRF, FRET, photobleaching, super-resolution (SIM, STED, STORM/PALM), and live-cell imaging. Applications include using fluorescent probes to analyze subcellular localization and live cell-translocation dynamics. We will be using a flipped classroom for the course in that students will watch iBiology lectures before class, and class time will be used for engaging in extensive discussion. Lab portion involves extensive in-class use of microscopes in the Stanford Cell Sciences Imaging Facility (CSIF) and Neuroscience Microscopy Core (NMS) microscopy facilities. 3 units, M. Teruel.

Offered Spring 2017-2018, MW 1:30-2:50PM, Li Ka Shing Center, Rooms 203/204 and 208, F 1:30-4:20PM.

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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

This course introduces the student to human neuroimaging using magnetic resonance scanners. 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 2017-2018, TTH 1:30-2:50PM, SAPP Center for Science Teaching and Learning, Room 105.


Psych 204B. Human Neuroimaging Methods

This course introduces the student to human neuroimaging using magnetic resonance scanners. 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. Required: Psych 204a; Recommended: Cognitive Neuroscience. 3 units, K. Grill-Spector.

Offered Spring 2017-2018, TTH 9:00-10:20AM, Jordan Hall, Bldg 420, Room 419.


Psych 204. Computation and Cognition: the Probabilistic Approach (CS 428)

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 units, E. Bennett and N. Goodman.

Offered Fall 2017-2018, TTH 1:30-2:50PM, Lane Hall, Bldg 200, Room 305.

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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 287. Brain Machine Interfaces: Science, Technology, and Application (NSUR 287)

This course explores the current state of brain-machine interfaces: technologies that directly stimulate and/or record neural activity. Such interfaces are being used to treat nervous system disorders, including hearing, seeing, and motor dysfunction. We expect that the range of applications will expand over the next decade to other neurological conditions and to augmentation of function. The material we cover aims to explain some of the existing technology and to clarify its limitations and promise. The course organization is designed to develop new ideas and promote new collaborations for extending the reach of these technologies. The class will feature lecturers with expertise in brain-machine interfaces of various sorts or related technologies and methods, as well as directed readings and discussion about new work in the field. In the previous year lectures were given by: Brian Wandell, Daniel Palanker, Nikos Logothetis, John Oghalai, Stephen Baccus, Paul Nuyujukian, Dan Yoshor and Nick Melosh. 1-3 units, EJ Chichilnisky and J. Gardner.

Offered Spring 2017-2018, TH 1:30-4:20PM, SAPP Center for Science Teaching and Learning, Room 104.


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)

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 2017-2018, T 1:00-2:20PM, F 10:00-11:20AM, 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

  • Jay McClelland (MBC director, committee co-chair)
  • Steve Baccus (committee co-chair)
  • Ivan Soltesz
  • Surya Ganguli


Program Manager

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