580.630 Theoretical Neuroscience (Spring 2008)

Course Syllabus

Time: M/W 8:30-9:45am

Location: Ames 219

Course Director: Prof. Xiaoqin Wang (xiaoqin.wang AT jhu.edu)

Instructors:

Prof. Xiaoqin Wang (xiaoqin.wang AT jhu.edu)

Prof. Eric Young (eyoung AT jhu.edu)

Prof. Kechen Zhang (kzhang4 AT jhmi.edu)

Course web site: http://webhost5.nts.jhu.edu/xwang/courses/580_630.html

Grading: Pass/Fail

Reference books:

Theoretical Neuroscience by Peter Dayan and L. F. Abbott (MIT Press, 2001)

Spikes: Exploring the neural code by Fred Rieke et al. (MIT Press, 1997)

Format and requirement:

Lectures by instructors. Students are required to complete three projects during the semester.

Subjects and schedule [Reading assignment]:

1/28 Introduction (Prof. Wang)

The role of theoretical tools in systems neuroscience

Methodological considerations in spike recordings [Lewicki 1998] [Quiroga et al. 2004]

Part I (Prof. Wang) Statistical methods in spike train analysis and signal detection theory

1/30 Poisson process as the model for spike trains: homogeneous and inhomogeneous Poisson processes [Johnson 1996]

2/4 Simulating point process [Johnson and Swami 1983]

2/6 No class (NIH meeting)

2/11 Analyzing spike data [Softky and Koch 1993]

2/13 Fundamentals of signal detection theory: the Gaussian model, decision criteria

2/18 No class (ARO conference)

2/20 No class (ARO conference)

2/25 Receiver operating characteristic (ROC) analysis, maximum likelihood estimation (MLE)

2/27 Applications to neural data [Britten et al. 1992]

[Project 1 Instructions]

Part II (Prof. Zhang) Network analysis and learning theory

3/3 Feedforward network: From perceptron to support vector machine

3/5 Recurrent network 1: Hopfield network and variants

3/10 Recurrent network 2: Continuous attractor models

3/12 Recurrent network 3: Oscillations and synchrony

3/17 No class (Spring break)

3/19 No class (Spring break)

3/24 Project 1 due

3/24 Unsupervised learning and reinforcement learning

3/26 Self-organization and map formation

3/31 Statistical theory of population coding

4/2 Theoretical issues of large-scale brain organization

[Project 2 Instructions]

Part III (Prof. Young) Non-linear system analysis and information theory

4/7 The concept of a receptive field, examples

4/9 Nonlinear systems, basic Wiener

4/14 Reverse correlation: from deBoer to the STRF
4/16 Project 2 due

4/16 Examples of receptive fields derived from reverse correlation. Does it work?
4/21 Basic information theory: The Gaussian channel
4/23 Application to neurons: S-R mutual information, bias

4/28 Systems redone: maximally informative dimensions, spike-based information

[Project 3 Instructions]

5/2 Project discussions (Prof. Wang)

5/12 Project 3 due