PH497/PH797/NBL400 Special Topics: Networks of Neurons (Spring, 2020)

Course Description: This is the second part of two-semester sequence on computational neuroscience. The first term was about the mathematical modeling of a single neuron. The second part is about the networks of neurons. Various computational models (MATLAB and Python) are used to simulate the firing patterns of neurons.

Learning Objectives: Learn mathematical and computational modeling of the folloiwngs:
  • Various types of synaptic connections
  • Connectivity Matrices and Motifs
  • Small clusters, Synchromization, Central Pattern Generators
  • Firing-Rate Models
  • More realistic Network Dynamics, Attractors and Chaos
  • Learning and Synaptic Plasticity, From Hebbian to Spike-Timing Dependent Plasticity

Day and Time: T. B. A.
Room: T. B. A.
Instructor: Dr. Ryoichi Kawai
phone: (205) 934-3931
skype*: userid = ryoichikawai
* You must send your skype user ID to the instructor by email in advance or your call will be blocked.
Office Hour: by appointment
Course Website: or ph797
Required Textbook  

An Introductory Course in Computational Neuroscience
by Paul Miller
The MIT Press, 2018
Supplemental Materials


MATLAB is used in all lectures. The use of MATLAB is required in this course. UAB has a site license and students are elligible to install MATLAB on their computer. Installation instruction is given at

Python is another popular computer language. Although we use MATLAB as main language in the lectures, Phython 3 is also supported. If you wish to use Python, Anadonda package is recommended for Microsoft Windows and Mac. (Linux is also supported by Anaconda but it is better to use the python package included in yourLinux distros.) The instructor uses Python 3 (Not Python 2). Download Anaconda for Python 3.7 at Every Linux distribution inlucdes Python 3. Use a software installer in your Liux system.

Several homework problems will be given. Homework must be turned in electronically by email.  Allowed formats are MATLAB script file (.m), Python script file (.py), source codes in other languages such as (.c) and (.f90) files so that the instructor can compile and excute the programs. Hand written documents must be scanned in PDF.

Some sections are lectured by students. Each students must present at least two major sections in the textbook.

There is no paper exam. Students must complete a computational project and submit a term paper by April 10. Students can work on a porject suggested by the instructors or propose a project of their own interest. The project must use sufficientl level of computational methods suggested by the instructor. The instructor will guide each project individually to make it sure that the project contains sufficient level of computational methods.

Attendance is required. To pass the course you must attend at least 80% of lectures. Excessive absence will result in F.

The project carries 50 pts, presentation, 30pts and homework 20 pts. The total maximum possible points is 100 pts. Letter grades are determined by the rule given in the table.

1Review of single neuron
2-4CH5: Connections between Neurons
5-8CH6: Firing-Rate Models and Network Dyanmics
9-11CH7: An Introduction to Dynamical Systems
12-15CH8: Learning and Synaptic Plasticity