PSYC 5270/BME 6086 - Statistical Analysis of Neural Data (Spring 2018)

Instructor: Ian Stevenson
ian.stevenson@uconn.edu
Office: BOUS 112
Office hours by appointment
Meets in BOUS 109 on Fri 10:10am - 1:10pm

This course aims to give students a practical introduction to the analysis of neural data. Topics will include time series, regression, clustering, and dimensionality reduction with an emphasis on how these techniques are used to interpret neural signals from membrane potentials and spikes to EEG and fMRI. Students will gain hands-on experience with these methods through problem sets and a course project.

Lectures and homeworks will use Matlab (available here or through Skybox).

Optional texts:
Matlab for Neuroscientists, 2nd Ed. Wallisch et al. 2013. Academic Press.
Information Theory, Inference, and Learning Algorithms. MacKay, 2003. Cambridge University Press.

Code:
GitHub
 
TopicAssignments & Optional Readings
01.19Logistics and introduction to the course

01.26Matlab Basics Discussion Question 1
Matlab Tutorial, Matlab for Neuro Ch 2

02.02Math, Probability, and Statistics Review Discussion Question 2
Linear Algebra, Probability, Shalizi Notes, Matlab for Neuro Ch 3

02.09Regression and Hypothesis Testing Discussion Question 3
Heeger Notes, Matlab for Neuro Ch 10

02.16Frequency Analysis 1 - Fourier Transforms Problem Set 1
Olshausen Notes, Chronux Tutorial, Matlab for Neuro Ch 11-12

02.23Frequency Analysis 2 - Filtering & Wavelets Discussion Question 4
Heeger Notes, Matlab for Neuro Ch 13

03.02No Class

03.09Neural Data Analysis "Journal Club"

03.16No Class - Spring Break

03.23Neural Coding Problem Set 2

03.30Unsupervised Learning Discussion Question 5
PCA Tutorial, Matlab for Neuro Ch 19

04.06Information Theory Discussion Question 6
ICA Tutorial, Matlab for Neuro Ch 20

04.13Bayesian Methods Discussion Question 7 (optional)
Olshausen Note

04.20Voltage Models Problem Set 3

04.27Final Presentations Project Reports Due May 4 @ 5p

Think of the class project as an extended lab assignment. This is a chance to explore the ideas covered in class in more depth or as they relate to your own work. In addition to giving an oral presentation on your project at the end of the semester, you should turn in a short report (5-10 pages) that describes what you did, why you did it, what you found, and why it matters.

At the end of the course everyone will give a short oral presentation on their project. The format should be similar to a short conference talk. Allow 20min for the presentation with an additional 10min for questions.

Open Data

Further Reading

Enrollment information

3 credits. Instructor permission required. Open to graduate students.

Grading

Based on discussion questions and participation (25%), problem sets (25%) and final project (50%).

Accessibility

Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Center for Students with Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Academic Student Code

Academic dishonesty of any type will not be tolerated. Students should refer to the Student Code for specific guidelines (see section on Academic Integrity).

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