Difference between revisions of "CSCI6508/NESC4177"

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== Neural Computation / Theoretical Neuroscience 2013 ==
+
== Neural Computation / Theoretical Neuroscience 2014 ==
  
 
=== Instructor ===
 
=== Instructor ===
 
Dr. Thomas Trappenberg  
 
Dr. Thomas Trappenberg  
  
−
Office: Room 4216 in Mona Campbell Building on Coburg RD (main) and Room 313 in Goldberg building (office hour)
+
Office: Room 4216 in Mona Campbell Building
  
 
Email: tt@cs.dal.ca
 
Email: tt@cs.dal.ca
  
−
TA: Paul Hollensen (paulhollensen@gmail.com)
+
TA: Farzaneh Sheikhnezhad Fard <farzaneh.shfard@gmail.com>
  
−
Office hour: Mondays 4:30-5:30 and after appointment (write email)
+
Office hour: After class and by appointment (write email)
  
 
=== Course Description ===
 
=== 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 back- ground.
+
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.
 +
 
 +
=== Announcements ===
 +
 
 +
Special Session with Farzaneh on using Matlab
 +
on Friday, Jan 17, in MC building, Room 4217 (knock on 3rd door from stairs to get in).
  
 
=== Schedule (tentative, can change) ===
 
=== Schedule (tentative, can change) ===
  
 
{| class="wikitable"
 
{| class="wikitable"
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!Date!!Content !! Reference !! Assignment out !! Assignment due
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!Date!!Content !! Reference !! Assignment
 
|-
 
|-
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| Jan 8 || Overview || Chapter 1
+
| Jan 7 || Overview || Chapter 1, [[Media:Chapter1.pdf| slides 1]] ||
 
|-
 
|-
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| Jan 10 || Neuron1: Overview and synaptic transmission|| 2.1, 2.2
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| Jan 9 || Neuron1: Overview and synaptic transmission|| 2.1, 2.2, [[Media:Chapter2.pdf| slides 2]]
 
|-
 
|-
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| Jan 15 || MATLAB 1: General programming || Appendix E  
+
| Jan 14 || MATLAB 1: General programming || Appendix E  
 
|-
 
|-
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| Jan17 || Basic Calculus || Appendix B || [[Media:Assignment1.pdf|Assignment 1]]
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| Jan16 || Basic Calculus || Appendix B || [[Media:Assignment114.pdf|Assignment 1]]
 
|-
 
|-
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| Jan 22 || MATLAB 2: ODE || Appendix E and B
+
| Jan 21 || MATLAB 2: ODE || Appendix E and B
 
|-
 
|-
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| Jan 24 || Neuron 2: Axon and conductance-based compartmental models || 2.3,2.4 || [[Media:Assignment2.pdf|Assignment 2]] || Assignment 1
+
| Jan 23 || Neuron 2: Axon and conductance-based compartmental models || 2.3,2.4 || [[Media:Assignment214.pdf|Assignment 2]]  
 
|-
 
|-
−
| Jan 29 || Spiking models || 3.1,3.2
+
| Jan 28 || Spiking models || 3.1,3.2, [[Media:Chapter3.pdf| slides 3]]
 
|-
 
|-
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| Jan 31 || Rate models || 3.3,3.4 || Assignment 3 || Assignment 2
+
| Jan 30 || Rate models || 3.3,3.4 || [[Media:Assignment314.pdf|Assignment 3]]
 
|-
 
|-
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| Feb 5 || Plasticity 1: associators and physiology || 4.1,4.2
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| Feb 4 || Plasticity 1: associators and physiology || 4.1,4.2, [[Media:Chapter4.pdf| slides 4]]
 
|-
 
|-
−
| Feb 7 || Plasticity 2: Mathematical descriptions || 4.3,4.4 || Assignment 4 || Assignment 3
+
| Feb 6 || Plasticity 2: Mathematical descriptions || 4.3,4.4 || [[Media:Assignment414.pdf|Assignment 4]]
 
|-
 
|-
−
| Feb 12 || Networks 1: Background || 5.1,5.2
+
| Feb 11 || Networks 1: Background || 5.1,5.2, [[Media:Chapter5.pdf| slides 5]]
 
|-
 
|-
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| Feb 14 || Network of Izhikevich neurons || 5.3 || Assignment 5 || Assignment 4
+
| Feb 13 || Network of Izhikevich neurons || 5.3 || [[Media:Assignment514.pdf|Assignment 5]]
 
|-
 
|-
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| Feb 19 || Multilayer Perceptron 1 || 6.1, 6.2
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| Feb 25 || Multilayer Perceptron 1 || 6.1, 6.2, [[Media:Chapter6.pdf| slides 6]] ||
 
|-
 
|-
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| Feb 21 || Multilayer Perceptron 2 || (6.3,6.4) or application || Assignment 6 || Assignment 5
+
| Feb 27 || Multilayer Perceptron 2 || (6.3,6.4) or application || [[Media:Assignment614.pdf|Assignment 6]] Changed Date
 
|-
 
|-
−
| March 5 || Self-Organizing Maps || 7.1,7.2
+
| March 4 || PPP (Phenomenal Perceptron Project)
 
|-
 
|-
−
| March 7 || Dynamic Neural Fields || 7.3-7.5 || Assignment 7 || Assignment 6
+
| March 6 || PPP (Phenomenal Perceptron Project)
 
|-
 
|-
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| March 12 || Attractor Networks 1 || 8.1,8.2
+
| March 11 || PPP (Phenomenal Perceptron Project)
 
|-
 
|-
−
| March 14 || Attractor Networks 2 || 8.3,8.4 || Assignment 8 || Assignment 7
+
| March 12 || PPP (Phenomenal Perceptron Project)
−
|-
 
−
| March 19 || System 1: Examples || 9.1-9.5
 
 
|-
 
|-
−
| March 21 || System 2: Reinforcement learning || 9.6 Assignment 9 || Assignment 8
+
| March 18 || Self-Organizing Maps || 7.1,7.2 [[Media:Chapter72014.pdf| slides 7 (2014)]]
 
|-
 
|-
−
| March 26 || Cognitive Brain 1: Competitive dynamics and dynamic networks || 10.1,10.2
+
| March 20 || Attractor Networks || 8.1-8.2 [[Media:Chapter8.pdf| slides 8]] || [[Media:Assignment714.pdf|Assignment 7]]
 +
|-
 +
| March 25 || Reinforcement learning || 9.6  [[Media:Chapter9.pdf| slides 9]]
 
|-
 
|-
−
| March 28 || Cognitive Brain 2: The anticipating brain || 10.3,10.4 || Assignment 10 || Assignment 9
+
| March 27 || Reinforcement learning || 9.6 || [[Media:Assignment814.pdf|Assignment 8]]
 
|-
 
|-
−
| April 2 || Outlook
+
| April 1 || Cognitive Brain 1: Competitive dynamics and dynamic networks || 10.1,10.2, [[Media:Chapter10.pdf| slides 10]]
 
|-
 
|-
−
| April 4 || Outlook
+
| April 3 || Cognitive Brain 2: The anticipating brain || 10.3,10.4 ||  
 
|}
 
|}
  
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T.P. Trappenberg (2010) [[Fundamentals of Computational Neuroscience (2nd Edition)|Fundamentals of Computational Neuroscience, 2nd edition]], Oxford University Press, ISBN13: 9780199568413, ISBN10: 0199568413.
 
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
 +
 +
=== Resources ===
 +
 +
Talk by Jeff Hawkins http:http://bit.ly/1kTaFsb
 +
 +
TED Talk by Ramachandran http://www.ted.com/talks/vilayanur_ramachandran_on_your_mind
 +
 +
<!-- [[Media:paper_instructions.pdf|A brief (biased) guide to writing scientific papers]].-->
 +
 +
Brief video on Hubel and Wiesel at http://www.youtube.com/watch?v=IOHayh06LJ4
  
 
=== Grading Scheme ===
 
=== Grading Scheme ===

Latest revision as of 18:36, 20 March 2014

Neural Computation / Theoretical Neuroscience 2014

Instructor

Dr. Thomas Trappenberg

Office: Room 4216 in Mona Campbell Building

Email: tt@cs.dal.ca

TA: Farzaneh Sheikhnezhad Fard <farzaneh.shfard@gmail.com>

Office hour: After class and 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.

Announcements

Special Session with Farzaneh on using Matlab on Friday, Jan 17, in MC building, Room 4217 (knock on 3rd door from stairs to get in).

Schedule (tentative, can change)

Date Content Reference Assignment
Jan 7 Overview Chapter 1, slides 1
Jan 9 Neuron1: Overview and synaptic transmission 2.1, 2.2, slides 2
Jan 14 MATLAB 1: General programming Appendix E
Jan16 Basic Calculus Appendix B Assignment 1
Jan 21 MATLAB 2: ODE Appendix E and B
Jan 23 Neuron 2: Axon and conductance-based compartmental models 2.3,2.4 Assignment 2
Jan 28 Spiking models 3.1,3.2, slides 3
Jan 30 Rate models 3.3,3.4 Assignment 3
Feb 4 Plasticity 1: associators and physiology 4.1,4.2, slides 4
Feb 6 Plasticity 2: Mathematical descriptions 4.3,4.4 Assignment 4
Feb 11 Networks 1: Background 5.1,5.2, slides 5
Feb 13 Network of Izhikevich neurons 5.3 Assignment 5
Feb 25 Multilayer Perceptron 1 6.1, 6.2, slides 6
Feb 27 Multilayer Perceptron 2 (6.3,6.4) or application Assignment 6 Changed Date
March 4 PPP (Phenomenal Perceptron Project)
March 6 PPP (Phenomenal Perceptron Project)
March 11 PPP (Phenomenal Perceptron Project)
March 12 PPP (Phenomenal Perceptron Project)
March 18 Self-Organizing Maps 7.1,7.2 slides 7 (2014)
March 20 Attractor Networks 8.1-8.2 slides 8 Assignment 7
March 25 Reinforcement learning 9.6 slides 9
March 27 Reinforcement learning 9.6 Assignment 8
April 1 Cognitive Brain 1: Competitive dynamics and dynamic networks 10.1,10.2, slides 10
April 3 Cognitive Brain 2: The anticipating brain 10.3,10.4

Textbook

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

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

Resources

Talk by Jeff Hawkins http:http://bit.ly/1kTaFsb

TED Talk by Ramachandran http://www.ted.com/talks/vilayanur_ramachandran_on_your_mind


Brief video on Hubel and Wiesel at http://www.youtube.com/watch?v=IOHayh06LJ4

Grading Scheme

Quizzes 35%, Assignments & Projects 65%

Academic Integrity & Plegarism

(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.

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