Background
Research
Teaching
Undergraduate
CISC 3310

CISC 2210


Graduate
Comp Sci 84030 & Psych 87100

Notes



Comp Sci 84030- Psych 87100 -    Computational Neuroscience

Theodore Raphan and Andrew Delamater

M 11:45 A.M-1:45 PM
Room 5383 - Graduate Center
Spring    2017

INSTRUCTOR OFFICE AND COMMUNICATION INFORMATION:

Office: TBD
Office Hours: TBD

1. E-mail related to this course should be sent only to:
mailto:raphan@nsi.brooklyn.cuny.edu
2. E-mail sent to any other address will be lost and you will
not receive a response.


IMPORTANT ADMINISTRATIVE INFORMATION

1. Establish a DropBox Account and Link it to my address.

    This way, when you place assignments in your Dropbox  folder , I will be able     to see them.

 

NOTEWORTHY DATES:


COURSE DESCRIPTION

This course will introduce students to the mathematical models, computational methods, and experimental basis for learning processes in biological systems (animals and humans) and how modeling these systems has impacted computational algorithms. The topics that will be covered in this course will include elements of statistical decision theory, adaptive control, machine learning approaches to pattern classification, and neural nets, and will provide a formal structure for solving problems in behavioral, physiological and representation-mediated behavior.

  SUGGESTED TEXTBOOKS

Serge Lang
Linear Algebra - Third Edition

Christopher M. Bishop
Pattern Recognition & Machine Learning
                   
GRADING

There will be a Midterm and Final and Homework Problems
The weighting will be Determined and you will be informed.


SYLLABUS
Week
Book Sections Topic
 Exercises/Readings
1
Lang- I Sec 1 and 2
Introduction and
Review of Linear Algebra
Exercises for Vector Spaces
and Matrices
Given in lecture Notes
2
Lang
Definition of Norm, inner product andMetric Space
Exercises for Inner product, Norm
Given in lecture Notes
3
Bishop- Chapter 1 and 2
Review of Probability and Statistics and Bayesian Methods in Identifying Pattern Classes
Exercises on Curve Fitting and using least Squares and Bayesian
4
Notes-Lecture 4
Classifying Patterns based on Discriminant Functions and Closeness, Supervised and Unsupervised Learning, Trainable Classifiers (Neural Nets), Perceptron Algoritm
Do Homework Assignments shown in Lecture 4 Notes. Go over Lecture 4 in Notes
5
Read Papers Located in Notes
Associative Learning Phenomena
and Principles
Nasser & Delamater, 2016
Rescorla, 1968
Williams, 1999
6
Read Papers
Located in
Notes
Rescorla-Wagner Model and Applications
Blough, 1975
Delamater, 2016
Rescorla and Wagner, 1972
7
Read Papers Located in Notes
Extension of 2-Layer Network to Exposure Effects
McLaren, Mackintosh, 2000
8
Study
Midterm
Will Cover lectures 1-7
9



10



11



12



13



14



15
Final




COURSE POLICIES Student Conduct

Any acts of disruption that go beyond the normal rights of students to question and discuss with instructors the educational process relative to subject content will not be tolerated, in accordance with the Academic Code of Conduct described in the Student Handbook

Electronic Devices in Class Policy

Cellular telephones, pagers, CD players, radios, and similar devices are prohibited in the classroom. Calculators and computers are prohibited during examinations, unless specified.


Examination Policy

A midterm and final examination will be given in class. Please schedule your other activities in advance. No make-up exams will be allowed without prior arrangements being made.

To prepare for examinations, read the chapters, go over the notes you take in class, and do the assignments. 100 % of the questions are taken directly from the reading material and what is covered in class.

Incomplete Policy

Students will not be given an incomplete grade in the course without sound reason and documented evidence. In any case, for a student to receive an incomplete, he or she must be passing and must have completed a significant portion of the course.


Academic Integrity Policy

Students are expected to uphold the school’s standard of conduct relating to academic honesty. Students assume full responsibility for the content and integrity of the academic work they submit. The guiding principle of academic integrity shall be that a student's submitted work, examinations, reports, and projects must be that of the student's own work. Students shall be guilty of violating the college’s policy if they:

  • Represent the work of others as their own.
  • Use or obtain unauthorized assistance in any academic work.
  • Give unauthorized assistance to other students.
  • Modify, without instructor approval, an examination, paper, record, or report for the purpose of obtaining additional credit.
  • Misrepresent the content of submitted work.

Any student violating the colleges academic integrity policy is subject to receive a failing grade for the course and will be reported to the Office of Student Affairs. If a student is unclear about whether a particular situation may constitute violation, the student should meet with the instructor to discuss the situation.

For this class, it is permissible to assist classmates in general discussions of computing techniques. General advice and interaction are encouraged. Each person, however, must develop his or her own solutions to the assigned projects, assignments, and tasks. In other words, students may not "work together" on graded assignments. Such collaboration constitutes cheating. A student may not use or copy (by any means) another's work (or portions of it) and represent it as his/her own. If you need help on an assignment, contact your instructor, not other classmates

Disabilities Policy

In compliance with the Americans with Disabilities Act (ADA), all qualified students enrolled in this course are entitled to “reasonable accommodations.” Please notify the instructor during the first week of class of any accommodations needed for the course

General Advice

  1. COME TO CLASS
  2. Take good notes.
  3. Ask questions.
  4. Do the assignments on time.
General Information

Students should prepare to spend at least 3 hours weekly on this material. If you do not have enough time, do not take the course!

  1. Contact me if you are confused or fall behind, for whatever reason. Come to my office hours or email me.
  2. I get MANY email messages every day, so please keep your message short and to the point. If your message is too long , I probably will not read it.
  3. Note that email messages where the sender's name seems fake (e.g., "Mickey Mouse") or the subject is blank or undecipherable, may be automatically filtered out in attempt to eliminate spam and other offensive messages.
  4. If I haven't replied to you, please be patient. Sending me multiple copies of the same message (or multiple messages that say the same thing) only clogs my inbox, which takes me longer to get to your message.
  5. PLEASE SIGN YOUR EMAIL and mail it to:
    mailto:raphan-3310@sci.brooklyn.cuny.edu
    This will insure that you are in the class and should be responded to. If you mail to any other address, it will probably not be answered as the system will assume it is SPAM.