2012 spring BiS427 Computational Neuroscience



"Computational Neuroscience¡° aims at introducing fundamental concepts and principles of neural information processes in the brain and its application to artificial intelligent systems to three-year or fourth-year undergraduate students. The topics studied in this class include information processing of single neurons (action potentials and their series of spikes) and information encoding and decoding, information processes of the neuronal networks and neural ensembles in various parts of the brain. In addition, Hebbean learning, reinforcement learning, Long-term potentiation and Synaptic plasticity, bursting and synchronization, and other significant issues are discussed. We provides with foundations of neuronal and network processes underlying human cognition, and then offers its application to real problems and engineering issues. Integrative neuroscience and systems neuroscience will be emphasized in this class.



Jeaseung Jeong (jsjeong@kaist.ac.kr, 042-350-4319)



ChungMoonSoul Building (E16) #220

Tue, Thu 9:00-10:15



3 Unints (3:0:3)



No prerequisite class



Mid-term Exam: 30%

Final Exam: 30%

Homework: 20%

Class participation: 20%


Office Hours

Thursday 14:00-15:00 (CMS building, #1109, 042-350-4319)



Jeong, Dong-Hwa

Jung, Jin-Gun



Fundamentals of Computational Neuroscience (Thomas P. Trappenberg, 2002)

Articles published in computational neuroscience journals


Lecture Schedule


1. What is the computational neuroscience?

2. Neurons and conductance-based models

3. Action potential: the language of the neurons the language of the neurons

4. Spiking neurons and response variability

5. Associators and Synaptic plasticity

6. Cortical Organization and simple networks

7. Feed-Forward mapping networks

8. Perceptron Example: Computational model for the Stroop Task

9. Basic concepts for neural network modeling

10. Unsupervised Learning: Self Organizing Maps

11. Support Vector Machine

12. Default mode network: Spontaneous fluctuations in fMRI brain

13. Reinforcement learning: computational modeling for reward systems