A/Prof. Andrew P. Paplinski

CSE2330 Introduction to Computational Neuroscience

The textbook:
• W.W. Lytton, From Computer to Brain: Foundations of Computational Neuroscience, Springer-Verlag New York Inc., 2002

Recommended books:
• T.P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press, 2002
• E.T. Rolls and A. Treves, Neural Networks and Brain Functions, Oxford University Press, 1998
• P. Dayan and L. F. Abbot, Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems, The MIT Press, 2001

"The genetic code was cracked in the mid-20th century. The neural code will be cracked in the mid-21st. Genetic science has given way to its applications in biotechnology and bioengineering. Neuroscience is still up-and-coming, the next big thing. Furthermore, as genetic manipulations and basic neuroscience add more raw facts to the broth, the need for meaning, structure, and theory becomes greater and greater. Enough information is coming together that the next generation of computational neuroscientists will make the leap into understanding. That means grant money, prizes, and fancy dinners! (If the movies are a guide, it also means evil robots, mind control, and dystopia, but let's not ruin the moment.)"

Practicals/tutorials

Please note that the attendance at practical classes is compulsory.
Demonstrate the results of your work to your tutor
Consultations: Friday, 10am-12, 75-190

Prac#   Prac time     Report due:
  1 --   (w2,3)   --  10/08  (w4)
  2 --   (w4,5)   --  24/08  (w6)
  3 --   (w6,7)   --  07/09  (w8)
  4 --   (w8,9)   --  21/09 (w10)
  5 -- (w10,11)   --  12/10 (w12)

1. Prac 1 -- Basic computational tools and concepts

2. Prac 2 -- Model of Limulus Vision
   Gray-scale apple

3. Prac 3 -- Learning and self-organization

4. Prac4 -- Associative Memory Networks
   Scripts A : trnglDefB.m, pltSetB.m, Alzh.m
   Scripts B : AlzhGUI.m, AlzhGUI.fig, trnglDef.m, HopfConv.m, plotSet.m

5. Prac 5 -- From soap to volts -- hardware of the brain
   Hodgkin-Huxley script.

Lecture Notes

Table of contents

1. Introduction. Unit outline

2. Computational Neuroscience and You. Based on Lytton's Chapter 2.

3. Based on Lytton's Chapter 3: Basic Neuroscience:
   Chapter 3 contents
   Related figures (Big: 7 MB)

4. Concept Neurons --- Introduction to artificial neural networks.
   Related to/based on Lytton's Chapter 4.
   Neurons of the world

5. Limulus --- Model of a simple vision system
   Related to/based on Lytton's Chapter 8.

6. Learning and Self-Organization
   Self-Organizing Maps
   Supervised learning
   Related to/based on Lytton's Chapter 9.

7. Associative Memories
   Related to/based on Lytton's Chapter 10.

8. From Soap to Volts — Hardware of the brain
   Related to/based on Lytton's Chapter 11.

9. Generation of Action Potential --- Hodgkin-Huxley Model
   Related to/based on Lytton's Chapter 12.

10. Compartment modeling --- Lytton's Chapter 13.
    Some additions to sec.13.3: Chemical synapse modeling