## APPM 4370/5370: Computational Neuroscience (Spr2020)

**Lectures**: MWF 11-11:50am, view online recorded videos given below

3/16: Neural networks [1.1-1.5] by Hugo Larochelle

3/18: Neural networks [1.6,2.1-2.3] by Hugo Larochelle

3/20: Neural networks [2.4-2.8] by Hugo Larochelle

3/30: Neural networks [2.9-2.11] by Hugo Larochelle and Perceptron + BackProp Intro (4370youtube)

Supplemental Videos from 3blue1brown: Backprop (Deep Learning Ch 3 + 4)

4/1: Backprop in two-layer perceptron + Motivating the sigmoid (4370youtube) and read jupyter notebook for backprop

4/3: Reservoir computing + Training recurrent neural networks (4370youtube)

4/6-4/27: Meet with me in zoom office hours (or setup a zoom appt by email) to discuss ongoing project work.

**Instructor**: Prof. Zack Kilpatrick (zpkilpat|at|colorado|dot|edu)

**Office Hours**: Mon 12-1:30pm, Tues 12-1pm, or by appt on zoom

Syllabus: Complete course information. COVID-19 Addendum

**Textbook (free)**: Neuronal dynamics: From single neurons to networks and models of cognition

by Wulfram Gerstner, Werner M. Kistler, Richard Naud, & Liam Paninski; Cambridge University Press (2014)

**Supplemental Texts:**

Theoretical neuroscience by Peter Dayan & Larry F. Abbott; MIT Press (2005) Chapter 7: Network Models

Spikes, decisions, and actions: Dynamical foundations of neuroscience

by Hugh R. Wilson; Oxford University Press (1999).

A short course in mathematical neuroscience by Philip Eckhoff & Philip Holmes

A probability primer by Bruno Olshausen

**Prerequisites**: Diff Eqns (APPM 2360 or equiv) and Matrix Methods (APPM 3310 or equiv).

Some background in probability (e.g., APPM 3570) recommended.

**Grading**: Your final grade is based on six HWs (60%); four non-collaborative problems (20%); and the final project (20%).

Grades are posted on canvas.

**Homework**: Problems are assigned via links below. Email me your homework by 11am on the due date.

**Late homework is not accepted.**Use either Scannable or Genius Scan if you are using your phone to take photos of your HW.

**python**: You will learn and use python to solve homeworks and in your project. Example code/jupyter notebooks are given at this github repo.

Projects: Click this link for a list of potential project assignments. You may also meet with me to discuss something different or select a refereed research paper to read, recapitulate, and extend.

**Exams?**There are no exams in this course! Just a final project and four non-collaborative problems on HW2, 3, 5, & 6.

***Assignments are due by 11am on the given date. HW2, 3, 5, & 6 have non-collaborative problems, which you may NOT discuss with your classmates. Solutions are posted on canvas.**

Homework 1: Due Fri 1/24

Homework 2: Due Wed 2/5

Homework 3: Due Wed 2/19

Homework 4: Due Wed 3/4

Homework 5: Due Wed 3/25

Homework 6: Due Wed 4/8