SNU Biointelligence Lab

2071.402 Brain and Computation (Spring 2009)

School of Computer Science and Engineering, Seoul National University

Instructor: Prof. Byoung-Tak Zhang
TA: Hoon-Hee Kim (Office: 138-409, Tel: 02-880-5890)
Classroom: 83-501
Time: Thursday 14:30–17:30

Course Objectives

To understand the principles of information coding and processing in the brain
To learn mathematical and computational tools for modeling brain function at the neuron and network levels
To build computational models of learning and memory in the brain networks

Textbooks

Fundamentals of Computational Neuroscience — Thomas Trappenberg — 2002

References

Principles of Neural Science — Kandel, Schwartz & Jessell — 2000
Nature Neuroscience 2000 supplement — Computational Approaches to Brain Function

Grading Policy

구분	비율
Mid-Term Exam	45%
Final Exam	45%
Presence & Participation	10%

Course Schedule

Topic	Description
Introduction	Overview of computational neuroscience
Neurons and Conductance-Based Models	Hodgkin-Huxley model, ion channels
Spiking Neurons and Response Variability	Integrate-and-fire models, noise
Neurons in a Network	Synaptic transmission, network dynamics
Representations and The Neural Code	Population coding, rate vs. temporal codes
Feed-forward Mapping Networks	Perceptrons, multi-layer networks
—	Mid-Term Exam
Associators and Synaptic Plasticity	Hebbian learning, LTP/LTD
Auto-associative Memory and Network Dynamics	Hopfield networks, attractor dynamics
Continuous Attractor and Competitive Networks	Place cells, winner-take-all
Supervised Learning and Rewards Systems	Backpropagation, reinforcement learning
System Level Organization and Coupled Networks	Cortical hierarchy, binding
—	Final Exam