Course Archives
Introduction to Computational Neuroscience
Fridays, 2:00 p.m. - 4:45pm EDT / EST, August 27 - December 3, 2004
This upper-level undergraduate/lower-level graduate course is being presented over the Access Grid, and will offer an introduction to the methods and techniques of computational neuroscience, with an emphasis on exposure to practical tools for performing simulation and analysis of biological systems. The focus will be on realistic modeling of neurons, and working with actual neural spike trains, as opposed to more abstract connectionist approaches to modeling.
OBJECTIVES:
By the end of this course students should:
- Understand the fundamentals of neural activity, including membrane characteristics and the role of ion channels.
- Understand how neurons can be computationally modeled, including how to select an appropriate level of modeling for the phenomenon being studied, and the tradeoffs that are involved.
- Understand ways in which neuron spiking patterns can encode sensorimotor information.
- Be familiar with a variety of software tools and databases available for research in computational neuroscience.
- Be able to use the NEURON simulation program to construct biologically realistic models of neurons and networks of neurons.
- Be able to use Matlab for neural spike train analysis.
INSTRUCTORS:
- Giorgio A. Ascoli, Ph.D., Associate Professor, Department of Psychology, George Mason University
- Graham Cummins, Ph.D., Postdoctoral Research Associate, Center for Computational Biology, Montana State University
- Alexander Dimitrov, Ph.D., Assistant Professor, Department of Cell Biology and Neuroscience, and Center for Computational Biology, Montana State University
- Michael Hines, Ph.D., Research Scientist, Department of Computer Science, Yale University
- Greg Hood, Neuroscience Specialist, Pittsburgh Supercomputing Center, Carnegie Mellon University
- Rob Kass, Ph.D., Professor, Department of Statistics, Carnegie Mellon University
- Tai Sing Lee, Ph.D., Associate Professor, Department of Computer Science and the Center for the Neural Basis of Cognition, Carnegie Mellon University
- John P. Miller, Ph.D., Director of the Center for Computational Biology and Professor, Department of Cell Biology and Neuroscience, Montana State University
- Thomas M. Morse, Ph.D., Postdoctoral Research Associate, Department of Neurobiology, Yale University
- Joel R. Stiles, M.D., Ph.D., Senior Scientific Specialist, Pittsburgh Supercomputing Center, and Associate Professor, Department of Biological Sciences, Carnegie Mellon University.
- John M. Sullivan, D.E., Director of the Center for Comparative NeuroImaging, and Associate Professor, Departments of Mechanical Engineering, Biomedical Engineering, and Electrical Engineering, Worcester Polytechnic Institute.
COORDINATING FACULTY:
- Aric Agmon, Ph.D., Assistant Professor, Department of Neurobiology and Anatomy, West Virginia University
- Jim Belanger, Ph.D., Assistant Professor, Department of Biological Sciences, Louisiana State University
- Ricardo Gonzalez-Mendez, Ph.D., University of Puerto Rico Medical Sciences Campus
- Julia S. Mullen, Ph.D., Academic Computing Application Scientist, Computing and Communications Center, Worcester Polytechnic Institute
PREREQUISITES:
- Biology: basic knowledge of cell structure
- Physics: knowledge of voltage, current, resistance, capacitance, and their relationships
- Math: differential equations, linear algebra
- Computer Skills: familiarity with Windows and/or Unix operating systems, some programming experience (e.g. C, C++, Java, Pascal, or Matlab script language)
TEXT:
- Dayan, Peter, and Abbott, L.F., Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2001.
- supplementary readings selected by the instructors
DAYS/TIMES:
Friday 2:00 - 4:45 PM (Eastern Time) (10-15 min. break around 3:15 PM)
SYLLABUS:
[Reading assignments for each lecture are indicated in brackets. TN refers to Theoretical Neuroscience, the primary text for this course.]
Aug 20 (F): AG test session (only AG operators should attend this)
Aug 27 (F): Course Introduction and Overview (All)
Each instructor participating in the course will introduce themselves and give a brief overview of their area of interest (10 min. each)
PART I: NEURAL MODELING AT THE CELLULAR LEVEL
Sep 3 (F): Introduction to neural modeling (Miller)
[TN: 1.1;
Tank, D. W., What details of neural circuits matter?
Sem. in Neurosci.1: 67-79 (1989). (available online here)]
Membrane dynamics, passive properties of neurons, single-compartment models, integrate-and-fire models (Hood)
[TN: 5.1 - 5.4]
Sep 10 (F): Voltage-dependent conductances, the Hodgkin-Huxley model of spike generation, synaptic conductances (Hood)
[TN: 5.4 - 5.10]
Introduction to the NEURON simulator; building a model of a single neuron, implementation of Hodgkin-Huxley model (Hines)
Sep 17 (F): Multicompartment modeling; cable theory; dendritic trees (Hood)
[TN: 6.1 - 6.5]
Construction of multicompartment models in NEURON (Hines)
Sep 24 (F): Network modeling (Hood)
[TN: 7.1 - 7.3]
Constructing synapses and networks in NEURON (Hines)
Oct 1 (F): Network modeling (cont.), phase plane analysis (Hood)
[TN: 7.4 - 7.7]
Introduction to biologically-realistic network models; in-depth example of a naturally-occuring network
(Cummins)
Oct 8 (F): Online databases of neural morphology and physiology, and how to use them (Morse)
Basics of parameter searching (Cummins)
Oct 15 (F): Midterm exam
PART II: NEURAL DATA ANALYSIS
Oct 22 (F): Neural coding, rate coding vs. synchronous coding, introduction to Matlab/Octave (Kass)
[TN: 1.1 - 1.3]
Statistical analysis of neural firing (Kass)
[TN: 1.4 - 1.6]
Oct 29 (F): Receptive fields, Wiener kernel methods 1 (Lee)
[TN: 2.1 - 2.4]
Receptive fields, Wiener kernel methods 2 (Lee)
[TN: 2.5 - 2.8]
Nov 5 (F): Information theoretic approaches to spike train analysis (Miller)
[TN: 4.1 - 4.4]
Available software for spike train analysis (Dimitrov)
Nov 12 (F): Decoding 1 (Kass)
[TN: 3.1 - 3.3]
Decoding 2 (Kass)
[TN: 3.4 - 3.5]
PART III: OTHER COMPUTATIONAL NEUROSCIENCE METHODS AND TOOLS
Nov 19 (F): Computational aspects of fMRI studies (Sullivan)
Large-scale modeling (Hood)
Nov 26 (F): No class (Thanksgiving week)
Dec 3 (F): Morphological analysis and model synthesis;
L-Measure/L-Neuron (Ascoli)
Computational neurophysiology; MCell (Stiles)
Dec 7-16: Final exam scheduled locally at each site
COURSE REQUIREMENTS (for those taking the course for credit):
- Computer lab assignments
- Midterm exam
- Final exam
SOFTWARE REQUIREMENTS FOR LAB ASSIGNMENTS:
- NEURON
- Matlab It should also be possible to use the Octave program (free) for the Matlab-dependent exercises. Installation of Octave is fairly easy on a Linux or Unix system, but to install this on Windows requires Cygwin, and some familiarity with Linux/Unix is helpful.
SPONSOR:
- This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 0231173 and the National Institutes of Health (NIH) under Grant Number RR06009. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, National Institutes of Health, or Carnegie Mellon University.
ACCESS GRID VIRTUAL VENUE:
Full Sail Room off of the ANL AG Lobby
Dec 3 only: Pittsburgh Supercomputing Center room off of the Instituion Lobby
LECTURES:
Lectures are available online in PowerPoint, PDF, or RealPlayer10 format to students enrolled in the course. Free players for RealPlayer10 are available on the Real webpage (scroll to the very bottom for Mac and Linux players).
To access the lectures click here, and, when prompted for a User Name and Password, enter cnscours for the User Name, and enter the password provided to you.
OPTIONAL READINGS:
- Bower, J.M. and Beeman, D., The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System, 2nd ed., Springer-Verlag, New York, 1998.
- Davis, G.W. and Murphey, R.K., A role for postsynaptic neurons in determining presynaptic release properties in the cricket CNS: evidence for retrograde control of facilitation, J. Neurosci. 13(9), 1993, pp 3827-3838.
- De Schutter, Erik, ed., Computational Neuroscience: Realistic Modeling for Experimentalists, CRC Press, 2000.
- Izhikevich, E.M., "Which Model to Use for Cortical Spiking Neurons?", IEEE Trans. on Neural Networks, Sept. 2004, to appear. (available online here)
- Kandel, E.R., Schwartz, J.H., and Jessell, T.M., Principles of Neural Science, 4th ed., McGraw-Hill, New York, 2000.
- Rieke, F., Warland, D., deRuytervanSteveninck, R., and Bialek, W., Spikes: Exploring the Neural Code, Bradford Books, 1999.
- Wormatlas, http://www.wormatlas.org. Specifically, check out a few of the neurons in the "Individual Neurons" section (e.g. AFDL, PVQL) for examples of morphology and connectivity information, and "The Mind of a Worm" online article.
SOFTWARE TOOLS FOR USE WITH ACCESS GRID:
Click here for some software we developed in conjunction with this course.
CONTACT:
For questions about this course, please contact Greg Hood (ghood@psc.edu).
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