CISC 3250: Systems Neuroscience

CISC 3250: Systems Neuroscience

Class times: Monday and Thursday, 10:00 – 11:15am, John Mulcahy Hall (JMH) 330
Instructor: Prof. Daniel D. Leeds (my homepage)
Office: JMH 332
E-mail:
Office hours: Mon 12-1pm, Thurs 1-2pm — updated from Syllabus!

Full syllabus is available here
Course announcements and assignments will be posted over the course of the semester.

Course text: No good book for our course. "Fundamentals of Computational Neuroscience" (suggested); "Computational Cognitive Science" (suggested)

Matlab: We will be introduced to the Matlab computing environment in this course and use it selectively in homework assignments across the semester. You may use the copy available in the JMH 330 computer lab, but it also would be beneficial for you to download the Student license for $50 from Mathworks.

Sections below:

Announcements
Supplemental readings
Slides
Resources
Assignments

Announcements:
May 1, 9:45am: The final exam is May 9, 10am-noon!
Apr 15, 12:15am: We will have class in the lecture room, JMH 330.
Apr 4, 12:30am: As announced last time in class: Exam 2 will be April 25; office hours today will be 12-1pm.
Mar 24, 11:55pm: Class Mon Mar 25 will be in our normal lecture location, JMH 330
Feb 4, 10:50pm: Exam 1 will be Feb 28 (covering material from HWs 1 and 2)
Feb 4, 9:50am: Class today will be in the lecture room, JMH 330. Class THURSDAY will be in the lab, JMH 302.
Jan 22, 5:30pm: Office hours this Thursday will be 12-1pm

Supplemental readings:
All readings are in Trappenberg, and they are optional.

Topic	Reading	Equations
Philosophy of neural modeling	Chapter 1: all (predominantly for perspective)
The neuron — biology and input/output behavior	Chapter 2: 2.1, 2.2 (through 2.2.2); Chapter 3: 3.1 (through 3.1.2), 3.3, 3.4.1, 3.4.5, 3.5	3.1-3.4; 3.33, 3.35; 3.44; 3.45, 3.46
Learning in the neuron	Chapter 4: 4.1, 4.3 (through page 101), 4.4.3	4.3, 4.8, 4.10; 4.24, 4.27, 4.28, 4.29
Neural systems and neuroanatomy	Chapter 5: 5.1
Representations in the brain	Chapter 7: 7.1, 7.5.1, 7.5.4	7.25-7.28, 7.40-7.42
Perception	(Chapter 10: 10.1 ... optional, we will cover a simpler model in class) Attention-related cellular mechanisms: 3.5.1, 3.5.3 Online chapter 6	3.45
Motor control	9.5, 9.6 (particularly 9.6.4)

Slides:
Check slides after each lecture - I sometimes update the slides with additional content based on class discussion

Lecture 1, The neuron biology and behavior.

Animation: movement of action potential down the axon

Lecture M, Intro Matlab; spikeExample.mat
Lecture 2, Learning in the neuron
Lecture M2, Matlab Lecture 2, Data for lecture
Lecture 3, Cortical organization and data formats
Lecture 4, Representations in the brain. V1 neuron responses -- check out first 3 minutes
Lecture M3, Matlab Lecture 3 --- linear algebra linAlgPractice.mat; 3D brain data, 4D brain data
Lecture 5, Perception. Example difference in hearing with one ear vs. two ears
Lecture 6, Motor control - example of failed error correction with cerebllar ataxia
Lecture 7, Memory

Resources:
Extra practice questions from lecture 1, slides 40-41
Below are exam 1 practice questions! Questions are randomly color-coded. I recommend trying to answer one set of colored questions first (for example, red questions), review the answers to those questions, and then move on to another set of colored questions (for example, green).
Note: The term "template recognition" used in one of the practice questions is synonymous with the term "prototype recognition."