View source for NESC4177/CSCI6508 (2016)

== Neural Computation / Theoretical Neuroscience 2016 ==

==== Instructors ====
Dr. Thomas Trappenberg <br>
Office: Room 4216 in Mona Campbell Building <br>
Email: tt@cs.dal.ca  <br>
TA: Paul Hollensen  <paulhollensen@gmail.com > <br>
Office hour: By appointment (write email)

==== Course Description ====
This course is an introduction to computational neuroscience and brain style information processing and includes an introduction to the MATLAB programming environment and some required mathematical background.

==== Material and announcements ====

 Jan 5: Overview and introduction (important class as I will explain how this course works!!!!!)
 [[Media:Chapter1_16.pdf|Chapter1]]  [[Media:Chapter2_16.pdf|Chapter2]]

 Jan 7: Matlab
           Install basic Matlab with the following packages 
                   Statistics and Machine Learning Toolbox
                   Curve Fitting Toolbox
                   Optimization Toolbox
                   Neural Network Toolbox
                   Signal Processing Toolbox
           and watch the following videos before class on Thursday:
                    http://www.mathworks.com/videos/getting-started-with-matlab-68985.html?s_cid=learn_vid
                    http://www.mathworks.com/videos/working-in-the-development-environment-69021.html?s_cid=learn_vid
                    http://www.mathworks.com/videos/writing-a-matlab-program-69023.html
                    http://www.mathworks.com/videos/working-with-arrays-in-matlab-69022.html
    Exersize: Write a program that multiplies two random matrices of rank 1000. This program 
              should use two different implementations. In one implementation you should use 
              the Matlab operator `*’, and in the other you should component-wise calculations 
              (use explicit loops over the specific indices of the matrices to calculate the 
              resulting matrix). Your program should report the elapsed time for both methods
              by using the Matlab commands tic and toc.	

 Jan 12: ODEs and numerical integration. 
               This topic is covered in Appendix B in our textbook. 
               The program example we worked on in class [[media:ode1.m|ode1.m]]. 
               As an exercise, try to solve the differential equation dx/dt= f(x,t) 
               with f (x, t) = 1 − x + t.

 Jan 14: ODE of spikes and synapses. 
               I revised the slides of the Hodgkin-Huxley model in [[Media:Chapter2_16.pdf|Chapter2]] 
               to match the simulation implementation [[Media:hh.m|hh.m]],  
               and I post here also the  [[Media:HodgkinHuxley52.pdf|1952 paper of Hodgkin and Huxley]].  
               I also provide and updated version of [[Media:EPSP2016.m|EPSP.m]]
               We will also start the [[Media:Project116.pdf|first project]] which is due Friday, January 22 at midnight.

 Jan 26: Simplified neuron models. 
               [[Media:Chapter3_16.pdf|Chapter3 slides]]
               Exercise: Reproduce Figure 3.18 with Izhikevich neurons and the rate model 

 Feb 2: Multilayer Perceptron. 
               [[Media:MLP2016.pdf|MLP summary]] and example [[Media:mlpANDonlineComponent16.m|online program]] and [[Media:mlpXORbatch16.m|batch program]]. 
               [[Media:project2.pdf|Project 2]] is due Tuesday Feb 9 for which you need the pattern files [[Media:pattern1.txt|pattern1]] and [[Media:pattern2.txt|pattern2]]

 Feb 11: I would like you to find a subject of your interest and a related paper that you should review. 
        Please find a paper that discusses a reasonable model.  The [[list of example papers]] can be start. 
        The ultimate goal is to try and implement a version of this model (likely simplified) for further 
        studies. After the reading week we will start reviewing the papers. Every student should give a 
       10 minutes presentation to explain the paper (and possible background). 
       Please let me know the paper you want to review.

 Feb 23: We talk about the basic autoassociator recurrent network (Chapter 8) and 
        start working on [[Media:project3.pdf|Project 3]].

 March 15: The final two projects are posted here. [[Media:project4.pdf|Project 4]] contains small examples of 
         reinforcement learning, dynamic neural fields, and for grad students some self organizing map.  
         [[Media:project5.pdf|Project 5]] is the final individual course project wich is different for CSCI 6508 and NESC 4177. 
         Both projects are due shortly before the end of this term, on Monday April 4.

 March 17: [[Media:RL116.pdf|RL manuscript]] and [[Media:RLmaze.m|corresponding code]]

 March 22: The [[Media:RL116.pdf|RL manuscript]] was updated. Also, here's a [[Media:paper_instructions.pdf|brief (biased) guide to writing scientific papers]].

==== Textbook ====

T.P. Trappenberg (2010) [[Fundamentals of Computational Neuroscience (2nd Edition)|Fundamentals of Computational Neuroscience, 2nd edition]], Oxford University Press, ISBN13: 9780199568413, ISBN10: 0199568413.

http://www.amazon.ca/Fundamentals-Computational-Neuroscience-Thomas-Trappenberg/dp/0199568413

==== Grading Scheme ====

In class projects 50%, paper review 10%, final paper 40%

== Culture of Respect  ==

"We believe inclusiveness is fundamental to education. We stand for equality.  Disrespectful behaviour - like misogyny* - in our classrooms, on our campus and in our community is unacceptable. "

For more information please visit the CoReCS web site (http://www.dal.ca/faculty/computerscience/about/respect.html) 

== Academic Integrity & Plagiarism ==

(Based on the sample statement provided at http://academicintegrity.dal.ca. Written by Dr. Alex Brodsky.)

Please familiarize yourself with the university policy on Intellectual Honesty. Every suspected case will be reported.

At Dalhousie University, we respect the values of academic integrity: honesty, trust, fairness, responsibility and respect. As a student, adherence to the values of academic integrity and related policies is a requirement of being part of the academic community at Dalhousie University.

 

==== What does academic integrity mean? ====

Academic integrity means being honest in the fulfillment of your academic responsibilities thus establishing mutual trust. Fairness is essential to the interactions of the academic community and is achieved through respect for the opinions and ideas of others. Violations of intellectual honesty are offensive to the entire academic community, not just to the individual faculty member and students in whose class an offence occurs. (see Intellectual Honesty section of University Calendar)

 

==== How can you achieve academic integrity? ====

• Make sure you understand Dalhousies policies on academic integrity.

• Give appropriate credit to the sources used in your assignment such as written or oral work, com- puter codes/programs, artistic or architectural works, scientific projects, performances, web page designs, graphical representations, diagrams, videos, and images. Use RefWorks to keep track of your research and edit and format bibliographies in the citation style required by the instructor (http://www.library.dal.ca/How/RefWorks)

• Do not download the work of another from the Internet and submit it as your own.

• Do not submit work that has been completed through collaboration or previously submitted for another assignment without permission from your instructor. • Do not write an examination or test for someone else.

• Do not falsify data or lab results.

These examples should be considered only as a guide and not an exhaustive list.

 

==== What will happen if an allegation of an academic offence is made against you? ====

I am required to report a suspected offence. The full process is outlined in the Discipline flow chart, which can be found at: http://academicintegrity.dal.ca/Files/AcademicDisciplineProcess.pdf and in- cludes the following:

1. Each Faculty has an Academic Integrity Officer (AIO) who receives allegations from instructors.

2. The AIO decides whether to proceed with the allegation and you will be notified of the process.

3. If the case proceeds, you will receive an INC (incomplete) grade until the matter is resolved.

4. If you are found guilty of an academic offence, a penalty will be assigned ranging from a warning to a suspension or expulsion from the University and can include a notation on your transcript, failure of the assignment or failure of the course. All penalties are academic in nature.

 

==== Where can you turn for help?====

• If you are ever unsure about ANYTHING, contact myself.

• The Academic Integrity website (http://academicintegrity.dal.ca) has links to policies, defini tions, online tutorials, tips on citing and paraphrasing.

• The Writing Center provides assistance with proofreading, writing styles, citations.

• Dalhousie Libraries have workshops, online tutorials, citation guides, Assignment Calculator, Ref- Works, etc.

• The Dalhousie Student Advocacy Service assists students with academic appeals and student discipline procedures.

• The Senate Office provides links to a list of Academic Integrity Officers, discipline flow chart, and Senate Discipline Committee.

== Request for special accommodation ==

Students may request accommodation as a result of barriers related to disability, religious obligation, or any characteristic under the Nova Scotia Human Rights Act. Students who require academic accommodation for either classroom participation or the writing of tests and exams should make their request to the Advising and Access Services Center (AASC) prior to or at the outset of the regular academic year. Please visit www.dal.ca/access for more information and to obtain the Request for Accommodation – Form A.

A note taker may be required as part of a student’s accommodation. There is an honorarium of $75/course/term (with some exceptions). If you are interested, please contact AASC at 494-2836 for more information.

Please note that your classroom may contain specialized accessible furniture and equipment. It is important that these items remain in the classroom, untouched, so that students who require their usage will be able to participate in the class.