Computational Neuroscience :: Syllabus

Term: Fall 2015
Instructor: Daniel Meliza (cdm8j)
Class times: Fr 9:00-11:30A, Gilmer 225
Collab site: PSYC 7559 Comp Neurosci
Office Hours: by appointment
Last revised: 7/2015

What is this course about?

In order to survive and reproduce in a complex and highly variable physical world, animals have to extract information from sensory inputs, make decisions about how to allocate their resources, and perform tightly choreographed sequences of motor behaviors. Brains have evolved to perform these computations reliably, flexibly, and efficiently. Our goal is to understand how these computations are implemented by circuits of neurons.

Course objectives

Our work in this course will focus on methods for quantifying, visualizing, and predicting neural activity. At the end of this course, you will be comfortable processing different kinds of neural and behavioral data, and analyzing these data using a broad range of phenomenological and biophysical models. Specifically, you will be able to:

Meeting the objectives

This course uses readings, short lectures, in-class discussion, and computer exercises to introduce major topics in computational neuroscience, survey and evaluate recent techniques, and provide practical experience in processing and analyzing neural data. You’ll receive feedback on your progress towards the objectives through class participation, homework assignments, and a capstone project in which you develop a novel analysis of a publicly-available neural dataset. These activities will contribute to your final score as follows:

In-class discussion and paper presentations (35%)

Each week we will read several research papers, reviews, or textbook chapters related to the topic under discussion. You should carefully read and analyze all the assigned material before class meets each week. Bring your reading materials and notes to class, and be prepared to ask questions of the instructor and each other. The class meeting will include a short lecture by the instructor followed or preceded by a student-led discussion of a paper related to the previous week’s topic.

Take-home analysis assignments (35%)

Each week you will be assigned an exercise in model building, data analysis, or model fitting. Many of these assignments will require computer programming to implement. You may use any language of your choice. Suggested languages with strong support for scientific computing include MATLAB, Python, and R. Assignments are posted each Friday and are due the following Thursday evening. Unless otherwise noted, you must complete the assignments without using third-party toolkits or packages for neural analysis. You may reference the source code, but you must implement the analysis yourself. You may work in teams, but each member of the team must submit a copy of his or her own work.

Capstone project (30%)

Data from published papers is in the public domain, and many experimental neurobiology labs make their recordings available online or will share them on request. Your final assignment is to apply a computational method from the course to a dataset from the literature that has not previously been analyzed using your chosen methodology. You’ll design and implement the analysis, then prepare oral and written reports. If your analysis yields an interesting result, you may be able to submit a manuscript for publication.

There are several steps in this assignment:

What materials will you need?

Introductory readings will be from the following texts:

These books will be placed on course reserve, or are available electronically through the course Collab page or VIRGO.

Additional readings will be posted on the course Collab page.

In addition, the following texts are recommended as primers on relevant math and programming topics

What is the class schedule?

This schedule will be updated with readings and presentations as the semester progresses. You may propose alternate topics or ask to spend more time on existing topics.

See the Assignments Page for links to weekly assignments.

Date Topic Background Reading Paper
8/28 Neural data, descriptive analysis DA1
9/4 Linear systems analysis DA2 Kilgard et al. J Neurophys 2001
9/11 Receptive fields Schwartz et al. 2009 Haag and Borst, J Neurosci 1997
9/18 Receptive fields and non-white stimuli Theunissen et al. 2001 Touryan et al, J Neurosci 2002
9/25 Dynamical systems and neuron biophysics S2 Atencio et al, Neuron 2008
10/2 Data assimilation (Henry Abarbanel) Toth et al. 2011 Meliza et al, Biol Cybern 2014
Knowlton et al. 2014
10/9 Ion channels S - 5.1-5.4, Appendix B
Present analysis plans
10/16 No class (Society for Neuroscience Conference)
10/23 Phenomenological neuron models S8 Drion et al, PNAS 2015
Izhikevich, Ch 3 (optional)
Initial data report due
10/30 Decoding models and information theory DA: 3.1-3.2, 4 Lynch and Houghton, Front Neuroinformat 2015
11/6 Parametric decoding models Bates, Ch 1 Osborne et al, J Neurosci 2004
Rose and Hindmarsh, Proc B 1989
11/13 Population codes DA: 3.3-3.5, 7.1-7.3 Jeanne et al, Neuron 2013
11/20 Circuit models DA: rest of Ch 7 OR S: 9 Britten et al, Vis Neurosci 1996
11/27 No class (Thanksgiving break)
12/4 Spike sorting
12/15 Final analysis report due