Andrew H. Fagg
This course focuses on the use of computational methods in neuroscience. Topics covered include the development and use of computer models at the biophysical, circuit, and system levels, including models of single neurons, neural circuits, models of learning, and representation of sensory and motor information. Students will learn how to use computational tools to produce simulations. The course will be largely self-contained with respect to the computational and mathematical skills required, although some minimal experience with computing will be useful. Prerequisites: permission of the instructor. 3 credits.
Meeting Times: Tuesday/Thursday 2:30-3:45
Meeting Location: CS 140
Class Web Page: http://www-anw.cs.umass.edu/classes/691c/
Office Hours: TBA
Grades will be based on class participation (30%), homework assignments (30%) and on the class project (40%).
Homework assignments will involve programming in your favorite language (I highly recommend matlab).
Students may work in pairs for the homework assignments, handing in a single report. Ideally, these pairs will be made up of students from different departments (we will resolve the pairing by the first homework assignment).
A total of 4 homework assignments (7.5% each) will be given during the
Assignments are due on the specified date by 5 pm.
The class project on a topic of the student's choosing (with approval from the instructor) will be worth a total of 40% of your grade. Topics must be selected by October 9th (a 1/2 page proposal is due on this day, worth 5% of the class grade). A list of suggested topics will be available before this day. A short oral status report and a preliminary written report will be due on November 1st (7%). A written draft of the final document (including some results) will be due on November 27th (8%). The final oral report will be due on December 11th and the final written version on Dec 20 (together these are worth 20% of your grade).
The class project should involve the reading of several modeling and experimental papers, and then implementation of a model. The model may be a re-implementation of an existing model, but must involve some novel manipulations.
As with the homework assignments, students may work in cross-departmental pairs. Ideally, these pairs will be different than for the homework assignments.
Class participation (30%) will be based upon involvement in class discussions and the presentation of at most two of the papers that we will be reading. Class presentations will involve the preparation of a set of slides that highlight the important points of the paper. Students not presenting will be expected to hand in a 1-paragraph summary of each paper. It is expected that this summary will include several critical questions which will be used as discussion points for the class.
Primary machine access will be through the CS department's Edlab. See the following page for information about your account:
|Class #||Date||Real||Topic||Reading||HW||HW Due|
|1||9/6 (Thu)||Introduction & plans|
|2||9/11 (Tues)||9/18||Mathematical Tools||dS-1|
|3||9/13 (Thu)||9/18,20||Motor Cortex I||Georgopoulos et al.; Caminiti et al.|
|4||9/18 (Tues)||9/25,27||Motor Cortex II
Scott & Kalaska
|HW1: population codes|
|5||9/20 (Thu)||9/27,10/2||Motor Cortex III
|Ajemian et al.;
|6||9/25 (Tues)||10/2,4||Motor Cortex IV
|Kakei et al.
Shah et al.
|7||9/27 (Thu)||10/9,11||Dorsal Premotor Cortex
|Mitz et al.; Fagg et al.|
|8||10/2 (Tues)||10/11,16||Neural Coding
|Anastasio et al.; Anastasio et al.||10/23||HW1|
|9||10/4 (Thu)||10/25||Basal Ganglia and Reinforcement Learning
|Schultz et al.;|
|10||10/9 (Tues)||Projects||HW2: leaky integrators|
|11||10/11 (Thu)||10/25,30||Basal Ganglia II
|Graybiel; Berns & Sejnowski|
|12||10/16 (Tues)||10/30,11/1||Grasp Perception
|Rizzolatti et al.; Oztop & Arbib||Project Proposals|
|13||10/18 (Thu)||11/15,20||Cerebellum (Josh, Rob)||Thach et al.; Miall et al.|
|14||10/23 (Tues)||11/20,27||Cerebellum II (George, Andy)||Barto et al.; Spoelstra et al.|
|Class #||Date||Real||Topic||Reading||HW||HW Due|
|16||10/30 (Tues)||11/27,29||Ions and Synapses||PNS 5 & 6||HW3: passive models|
|17||11/1 (Thu)||11/8||Project reports||Oral project report + written outline|
|18||11/6 (Tues)||11/29,12/4||Circuit models||PNS 7 & 8|
|19||11/8 (Thu)||Markov models||dS 2|
|20||11/13 (Tues)||12/6||Voltage-dependent conductances||dS 5||Written draft|
|21||11/15 (Thu)||Unplanned||HW4: HH model||HW3|
|22||11/20 (Tues)||Active neurons||dS 9|
|23||11/27 (Tues)||Small neural circuits||dS 10||Draft of written report|
|25||12/4 (Tues)||Long term potentiation||Paulsen & Sejnowski; Rao & Sejnowski||HW4|
|26||12/6 (Thu)||Large neural circuits||dS 11|
|27||12/11 (Tues)||Final oral reports|
|28||12/13 (Thu)||Final oral reports|
|-||12/20 (Thu)||Final project reports due|
A. P. Georgopoulos, J. F. Kalaska, R. Caminiti and J. T. Massey (1982), On the Relations Between The Direction of Two-Dimensional Arm Movements and Cell Discharge in Primate Motor Cortex , Journal of Neuroscience, 2:1527-1537
R. Caminiti, P. B. Johnson, C. Galli, S. Ferraina and Y. Burnod (1991), Making Arm Movements Within Different Parts of Space: The Premotor and Motor Cortical Representations of a Coordinate System for Reaching to Visual Targets, Journal of Neuroscience, 11:1182-1197
F. A. Mussa-Ivaldi (1988), Do Neurons in the Motor Cortex Encode Movement Direction? An Alternative Hypothesis, Neuroscience Letters, 91: 106-111
S. H. Scott and J. F. Kalaska (1997), Reaching Movements with Similar Hand Paths but Different Arm Orientations. I. Activity of Individual Cells in Motor Cortex, Journal of Neurophysiology 77:826-852
R. Ajemian, D. Bullock, AND S. Grossberg (2000), Kinematic Coordinates In Which Motor Cortical Cells Encode Movement Direction, Journal of Neurophysiology, 84: 2191-2203
E. Todorov (2001), Cosine Tuning Minimizes Motor Errors, to appear in Neural Computation
S. Kakei and D. S. Hoffman and P. L. Strick (1999), Muscle and Movement Representations in the Primary Motor Cortex , Science, 285:2136-2139
Shah, A. and Fagg, A. H. and Barto, A. G. (in preparation), A Model of Primary Motor Cortex Recruitment for the Production of Wrist Movements
A. R. Mitz, M. Godshalk, S. P. Wise (1991), Learning-Dependent Neuronal Activity in the Premotor Cortex, Journal of Neuroscience, 11(5):1855-72
A. H. Fagg, M. A. Arbib, (1992), A Model of Primate Visual/Motor Conditional Learning, Journal of Adaptive Behavior, 1(1):3-37
T. J. Anastasio, D. A. Robinson (1990), Distributed parallel processing in the vertical vestibulo-ocular reflex: learning networks compared to tensor theory, Biological Cybernetics, 63(3):161-7
T. J. Anastasio, P. E. Patton, K. Belkacem-Boussaid (2000), Using Bayes' Rule to Model Multisensory Enhancement in the Superior Colliculus, Neural Computation, 12(5): 1165 -1187
W. Schultz (1998), Predictive Reward Signal of Dopamine Neurons, 80: 1-27
ONE MORE TBA
M. Graybiel (1998), The Basal Ganglia and Chunking of Action Repertoires, Neurobiology of Learning and Memory 70:119-136
G. S. Berns and T. S. Sejnowski (1998) A computational model of how the basal ganglia produce sequences, Journal of Cognitive Neuroscience 10(1):108-121
G. Rizzolatti, L. Fogassi, V. Gallese, (2001), Neurophysiological mechanisms underlying the understanding and imitation of action, Nature Reviews Neuroscience. 2(9):661-70
Oztop, E. and Arbib, M. A. (2001), Schema Design and Implementation of the Grasp-Related Neuron System, submitted
W. T. Thach, H. P. Goodkin, J. G. Keating (1992), The cerebellum and the adaptive coordination of movement, Annu Rev Neurosci 15:403-42
R. C. Miall, D. J. Weir, D. M. Wolpert, & J. F. Stein. (1993). Is the cerebellum a Smith predictor? Journal of Motor Behavior, 25:203-216
A. G. Barto, A. H. Fagg, N. Sitkoff, J. C. Houk, (1999) A Cerebellar Model of Timing and Prediction in the Control of Reaching, Neural Computation 11:565-594
J. Spoelstra, N. Schweighofer, M. A. Arbib (2000), Cerebellar learning of accurate predictive control for fast-reaching movements, Biological Cybernetics, 82(4):321-333
O. Paulsen, T. J. Sejnowski (2000), Natural patterns of activity and long-term synaptic plasticity Current Opinion in Neurobiology, 10:172-179
R. P. N. Rao, and T. J. Sejnowski (2001), Spike-timing dependent Hebbian plasticity as temporal difference learning, Neural Computation, 13(10):2221-2237 (*** will appear on Neural Computation Website)
This document was generated using the LaTeX2HTML translator Version 98.1p1 release (March 2nd, 1998)
Copyright © 1993, 1994, 1995, 1996, 1997, Nikos Drakos, Computer Based Learning Unit, University of Leeds.
The command line arguments were:
latex2html -no_navigation -split 0 -t CMPSCI 691C: Computational Neuroscience Syllabus -dir html -no_reuse -tmp /tmp syllabus.tex.
The translation was initiated by Andrew H. Fagg on 2001-11-19