**Biological Neural Computation** (BME 572)

Spring semesters

Time: Mondays and Wednesdays; 11:30-12:50 pm,Location: Whitaker 218

**Sample syllabus:**

Here is a link to the course syllabus for **Spring 2023**.

**Course Overview:**

This course will consider the computations performed by the biological nervous system with a particular focus on neural circuits and population-level encoding/decoding. Topics include, Hodgkin-Huxley equations, phase-plane analysis, reduction of Hodgkin-Huxley equations, models of neural circuits, plasticity and learning, and pattern recognition & machine learning algorithms for analyzing neural data.

Note: Graduate students in psychology or neuroscience who are in the Cognitive, Computational, and Systems Neuroscience curriculum pathway may register in L41 5657. For non-BME majors, conceptual understanding, evaluation and selection of right neural data analysis technique will be stressed.

L41 5657 prerequisites: Permission from the instructor

**Course Goals:**

The objectives of this course are to:

- To integrate previous math, physical science, biology and engineering studies into a rigorous investigation of the quantitative foundations of neurophysiology
- Provide students with quantitative tools essential for systems-level investigation of neural circuits
- Introduce fundamental pattern recognition and machine learning concepts required for neural data analysis

**Prerequisites:**

Calculus, Differential Equations, Basic Probability and Linear Algebra

**Optional Textbooks:**

There is no required text book for this course. Some optional books for references are:

Biophysics of Computation, C. Koch

Theoretical Neuroscience, L. Abbott & P. Dayan

Pattern Recognition and Machine Learning, Christopher Bishop

Principles of Neural Science, E. Kandel

Pattern Classification, Duda, Hart and Stork

Mathematics for Neuroscientists, Gabbiani and Cox

**Instructor:**

**Barani Raman, office:**Brauer 2007, email: barani@wustl.edu

**Grading**

- Point distribution
- 60% for homework
- 30% final project
- 10% class participation

Grading will be straight scale (91 – 100%: A; 81 – 90%: B; 71 – 80%: C; 61 – 70%: D; < 60%: F)

**Homework submission**

Homework assignments are due at 11:59 PM on the due date (soft copy should be uploaded to Canvas by the deadline; Hard copies should be handed over at the beginning of next in-class lecture). I will provide more details when I issue the first homework. With the exception of MATLAB’s built-in functions (e.g., cov, eig, mvnrnd), you are expected to write your own implementation of the algorithms; in case of doubt please consult with the instructor or the TAs.

**Paper Reviews**

A good habit that every graduate student (or those aspiring to get into grad schools) should nurture is reading papers and assimilating new ideas. To develop this aspect of graduate education, we will have a paper(s) associated for reading with each lecture. To facilitate healthy discussion in the lecture, it is strongly encouraged that you read these papers before each lecture.

**Late submissions**

Late submissions will receive a 10% penalty on the total grade of the assignment; the penalty will increase by an additional 10% every 24 hours. Hard copies of late submissions must be dated and time stamped by the instructor or the teaching assistants. An assignment is considered submitted when ALL components of the assignment have been submitted; e.g., late submission of one problem in a homework will cause your entire homework to be considered as a late submission.

**Project**

The course involves a semester long project. Interdisciplinary teams of comprising of 2-3 students are supposed to pursue an idea of your choosing. The chosen projects must be at a systems level and ideally will integrate multiple components of the course: biophysics of computation, neuron network modeling, data analysis using machine learning algorithms.

**Collaboration vs. Academic Dishonesty**

In the spirit of academic openness, students are encouraged to share learning experiences with one another. Discussion of (NOT collaboration on) homework and projects is strongly encouraged (write the names of your discussants on each assignment). All written work must be generated by yourself. Exam work should be your own with no discussion, even for take-home exams. Violations of academic integrity by any student will be handled according to the guidelines laid out for all Washington University students:

http://www.wustl.edu/policies/undergraduate-academic-integrity.html

Exceptions to any of the policies outlined in this syllabus for an individual student (e.g., due dates and times) will be handled on a case-by-case basis and possibly put to the remainder of the class for evaluation. No non-emergency deadline extension of any sort will be granted without the submission of a partially completed assignment.