Seminar - Computational Neuroscience

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.

Class Details

Meeting Times: Tuesday/Thursday 2:30-3:45

Meeting Location: CS 140

Class Web Page:

Office Hours: TBA

Texts and Other References


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 semester. 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.

Computer Access

Primary machine access will be through the CS department's Edlab. See the following page for information about your account:

Class Schedule

The actual schedule may be adjusted as the semester progresses. Schedule changes will be announced in class, and the web page will be updated accordingly.

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
Ashvin Shah
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.    
15 10/25 (Thu)   Unplanned     HW2

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    
holiday 11/22 (Thu)          
23 11/27 (Tues)   Small neural circuits dS 10   Draft of written report
24 11/29 (Thu) - Unplanned      
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

Daily Plan

Introduction and procedures. What do neurons do? Cortical anatomy.

Tools: Differential equations and solving; parameter fitting. de Schutter, Chapter 1 (24pg).

Case study: The involvement of motor cortex in the production of movements. Coordinate systems and cellular coding of information.

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

Motor cortex II

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

Motor cortex III

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

Motor Cortex IV

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

Premotor Cortex and the Production of Movement

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

Principles of neural coding

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

Basal Ganglia and reinforcement-based learning

W. Schultz (1998), Predictive Reward Signal of Dopamine Neurons, 80: 1-27


Project Proposals

Basal Ganglia II

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

Grasp Perception

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

Cerebellum and Coordinated Motor Learning

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

Cerebellum II

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


Ions and Synapses PNS Chapters 5 and 6.

Project status reports

Circuit models and passive properties. PNS Chapters 7 and 8.

Markov models of signalling pathways. de Schutter, Chapter 2 (24pg).

Comparative approaches to modeling voltage-dependent conductances. Focus on the HH model. de Schutter, Chapter 5 (30pg).


Active neurons. de Schutter, Chapter 9 (26pg).

Small neural circuits. de Schutter, Chapter 10 (30pg).


Models of Long Term Potentiation

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)

Large neural circuits. de Schutter, Chapter 11 (27pg).

Final presentations
Final presentations cont

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Seminar - Computational Neuroscience

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