This tentative schedule is likely to change once I start teaching
Any questions can be addressed to me at harel.shouval@uth.tmc.edu
or by phone to: 713-50-5708
Harel
ME 385J/GS140033: Introduction to Theoretical/Computational Neuroscience
Spring 2005 - Mondays 2:00 - 5:00 PM beginning Jan 24 2005
In Houston Room MSB G500, In Austin ETC 2.146
Course Director/Instructor: Harel
Shouval, Ph.D.
For More Information: Call 713-500-5708 or via email at harel.shouval@uth.tmc.edu
Course Description
This course provides an introduction to basic mathematical and computational methods of theoretical neuroscience. Different topics including the biophysics of single neurons, population coding, synaptic plasticity and learning will be covered. The course will rely on elementary mathematical methods such as linear algebra and calculus; most assignments will be Matlab based.
Course Requirements
One semester of college level calculus and linear algebra, as well as some programming experience.
Required Textbooks
Theoretical Neuroscience by Peter Dayan and L. F. Abbott.
Spring 2005 Estimated Schedule
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Additional suggested references:
2. Gerstner and Kistler, Spiking Neuron models
3. Hertz, Krough, Plamer: Introduction to the theory of neural computation
4. Johnston, Wu: Foundations of Cellular Neurophysiology
5. Koch: Biophysics of Computation
6. Tuckwell: Introduction to theoretical neurobiologyOffice hours:
When at Austin, Monday 11-1, ENS 616.
The TA is Jeff Gavornik
Email: gavornik@mail.utexas.edu
Grades:
Homework: 50%, midterm 20%, final 30%
Homework.
There will be 6 assignments; the best 5 assignments will be used for the grade. A delay of 1 day will result in -10 pt. Assignments with a delay of more than 1 day will not be accepted unless the teacher has given special premission.
Most homework problems will be based on Matlab simulations. Each assignment should be accompanied by a written report, explaining the findings and how to run the simulations.
No use of the Neural Network toolbox is allowed, and copying assignments from the web are not allowed. Students can work in groups but must submit separate and distinct codes and programs.
There will be some changes this year, but this can still be used
Class 1 (1/24/04)
Home work due 2/6/05.
Assignment 1a (30 pt):
program in matlab a preceptron with a perceptron learning rule
and solve the OR, AND and XOR problems. (due before Feb 6).Do not use NN toolbox
Assignment 1b (30 pt):
Implement a one layer linear and sigmoidal network, fit a 1D, a linear, a sigmoid and a quadratic function, for both networks. (Clarification – this is a network with 1 input and 1 output).
Assignment 1c (30 pt):
Program a 2 layer network in matlab, solve the XOR problem. Fit the curve: x(x-1) between 0 and 1, how many hidden units did you need?
Assignment 1d (10 pt):
If the sigmoind is replaced by a linear function, what type of problems can it solve? Can it solve the XOR problem. (explain in writing).
Class 2 (1/31/05)
Homework for 1/13
Assignment 2: (due Feb 13’th)
Program in matlab a Hodgkin-Huxley neuron with parameters from
DA- page 172. What happens if the Potassium conductance is set to 0?
Explore different input currents for different durations.
Is there a threshold voltage at which an AP is induced.
Program a Connor-Stevens model, DA- page 196, compare to the HH model. What is the difference between a type I and a type II response?It is best not to use an ODE solver.
Here it will be sufficient to use a first order method such as the forward Euler methodPossible texts for numerical ODE solutions:
Remember - first homework due 1/6
Appendix A of Chapter 5 in the DA book
Chapter 14 in the book Methods in Neuronal modeling by Koch and Segev, second edition. Chapter written by Mascagni and Sherman)
Class 3 (2/7/05)
HH- dimensionality reduction and phase plane analysis
Class 4 (2/14/04)
Cable equations - continued (see class 3)
HW-3 (due Feb 28):
a. implement a 2 state voltage dependent stochastic channel, average over many and compare to the analytic solution.
Assume a voltage step of 10mV, alpha=0.04*V, beta=0.2b. Implement a stochastic and deterministic potassium channel – average over many, compare.
Note: for b use potassium channel alpha and beta, eq 5.22 on page 171 of D&A. Use 1,10 and 100 channels.
Class 5 (2/17/04)
Class 6 (2/28/05)
Receptive fields and reverse correlation
Class 7 -midterm
Spring Break
Class 8 - (3/21/05) - reverse correlation and simple networks
Homework 4 due March 28.
Download the file below. It includes a sequence of random 'white noise' inputs and the corresponding outputs (res). Using reverse correlations induce the spatio-temporal kernal. Is it seperable? If it is can you guess an exact form for the spatial and temporal kernals?
Extra credit: (50%) Program a complex cell using an energy model with shifted Gabor filters. Try to use reverse correlation to find the spatio-temporal kernal of this neuron. Explain in detail why you get these results. Here the writeup counts at least as much as the program.
Class 9 (3/28/05)
1) Review of networks from previous week.
2) Abstract associative memory networks - update in class next week.
Download Excitatory Inhibitory network
Class 10 (4/4/05)
Homework 5: (due 4/18)
5a) Implement a simple Hebb neuron with random 2D input, tilted at an angle, of 30 degrees, with variances 1 and 3 and mean 0. Show the synaptic weight evolution. (200 patterns at least, small learning rate)
5b) Calculate the correlation matrix of the input data. Find the eigen-values, eigen-vectors of this matrix. Compare to 5a.
5c) Repeat 5a for an Oja neuron, compare to 5b.
Class 11 (4/11/05)Download Book Cahapters I-III (BCM)
Class 12 (4/18/05)
The biophysical basis of synaptic plasticity (either this week or next).
Class 13 (4/25/05)
Below this line, last years course
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Class 7 (3/2/04)
Class 8 (3/10/04)
Class 12 (4/6/04)
Download chapter and start reading before the next class !
Class 13 (4/13/04)
Class 14 (4/20/04)