Course Information

Introduction to Neural Coding and Computation

AMATH 342

M, 3:30-5:50

Prof. Eric Shea-Brown

Email: etsb@uw.edu
Office Hours:  M 9:45-10:45, Lewis Hall 325

TAs:  

Alex Johnson (verano13@uw.edu) 

Office Hours:  

Rhea Grover (rmgrover@uw.edu) 

Office Hours:  

About the Course

Required Texts

Required texts: "Theoretical Neuroscience" by Abbott and Dayan.

Optional reference:   "An Introductory Course in Computational Neuroscience" by Miller.

Readings from the literature will be provided with several modules as well.

A free very introductory overview of neuroscience is in "Brain Facts," linked here -- can serve as a quick optional reference and treatment of much brooder issues than we cover here.

This is a recommended and freely available PYTHON reference:  https://jakevdp.github.io/PythonDataScienceHandbook/ by Jake van der Plas.

Computing

Please bring to class a laptop with, installed, a recent (open-source and free) version of Python (Python3) and Jupyter Lab.  We recommend the  ANACONDA python distribution, but others are possible as well.  We'll use this frequently in class and on HW assignments; no Python or coding knowledge is assumed however, and tutorials will be provided to take you through the Python you'll need from scratch.

We'll make use of dropbox for some file transfer; you may optionally install this as well.

The text books provide the code needed to generate each figure in MATLAB, from their websites.  MATLAB has a very similar syntax to Python (in fact a bit simpler, and with a few differences highlighted in the "MATLAB gotcha" comments in the AMATH 342 Ipython tutorials).  An AMATH 342 MATLAB tutorial -- as well as MATLAB versions of most AMATH 342 lecture codes -- is provided to you at this dropbox link.   MATLAB is freely available via link here to all UW students.

Course topics

(1) Neural coding -- response statistics and signal decoding

• Introduction to neurons and spike trains
• Tuning curves
• Introduction to probabilty
• Neural responses and response variability
• Response to high-dimensional stimuli: spike triggered averages and effective filters
• The decoding problem, maximum likelihood and signal detection solutions
• Higher, and hierarchical, signal encoding

(2) Models of neuron spiking and feature "selection" and coding

• Circuit models of neurons as differential equations
• Basic numerical schemes for differential equations
• Input filters and feature selection
• Conductance and current based models for neural inputs
• Hodgkin-Huxley and reduced models for neural spiking

(3) Synaptic dynamics and neural networks

• Synaptic models
• Short-term synaptic plasticity
• Facilitation, depression, and the Tsodyks-Markram model
• The connectome
• Recurrent neural networks and basic computations on stimuli
• The perceptron and deep(er) neural networks

(4) Population coding: Modern large-scale recordings from cortex and beyond

Grading

Course grades are as follows.  They are in part based on Problem Sets due on Mondays at 3:15 (35%).  These Sets will combine programming, analytical work, and scientific reasoning.  Late HW is only accepted in unusual personal circumstances (including illness, family situation, academic travel, an unusual cluster of exams and/or important interviews, etc) in a given week (please reach out to the instructor).   Additionally, brief quizzes will account for 30% of the grade; these are done in class at 3:30 PM. Quizzes are "closed everything" (closed book / closed notes, and closed to access to any electronic files or the web).  An in person final exam will account for the remaining 35% of the grade; this is also "closed everything."

Important formatting instructions for problem sets:  please present your solutions as a neat writeup with your calculations, results, plots, and analysis for each problem together (Problem 1, Problem 2, ...).   Please put your original code for all problems together as an "appendix" to the entire document, at the end ("Code for all problems."). The combined document should be uploaded to canvas as a single .pdf.  Thank you!

Academic Integrity

Working together on problem sets is encouraged.  The work you turn in on problem sets should be your own understanding and calculations.  Please do not refer to previous years' solutions.  

Quizzes and the exam are individual work -- no collaboration allowed.  Please do not refer to previous years' quiz materials.  Quizzes and the exam are closed notes and textbook, and closed to all other materials (i.e. no web searches, no running python, or other access to any other materials during quizzes).  

University practices and policies

Access and Accommodations

Your experience in this class is important to me. If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.  If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations, you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions.  Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS.  It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Religious Accommodations

Required Syllabus Language: “Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy.  Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Notice to Students - Use of Plagiarism Detection Software

Notice: The University has a license agreement with SimCheck, an educational tool that helps prevent or identify plagiarism from Internet resources. Your instructor may use the service in this class by requiring that assignments are submitted electronically to be checked by SimCheck. The SimCheck Report will indicate the amount of original text in your work and whether all material that you quoted, paraphrased, summarized, or used from another source is appropriately referenced.