Difference between revisions of "NESC4177/CSCI6508 (2016)"
| Line 29: | Line 29: | ||
| Jan 12 || MATLAB 1: General programming || Appendix E | | Jan 12 || MATLAB 1: General programming || Appendix E | ||
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| − | | Jan14 || Basic Calculus || Appendix B || | + | | Jan14 || Basic Calculus || Appendix B || |
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| Jan 19 || (Paul) MATLAB 2: ODE || Appendix E and B | | Jan 19 || (Paul) MATLAB 2: ODE || Appendix E and B | ||
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| − | | Jan 21 || Neuron 2: Axon and conductance-based compartmental models || 2.3,2.4 || | + | | Jan 21 || Neuron 2: Axon and conductance-based compartmental models || 2.3,2.4 || |
|- | |- | ||
| Jan 26 || Spiking models || 3.1,3.2, [[Media:Chapter3.pdf| slides 3]] | | Jan 26 || Spiking models || 3.1,3.2, [[Media:Chapter3.pdf| slides 3]] | ||
|- | |- | ||
| − | | Jan 28 || Rate models || 3.3,3.4 || | + | | Jan 28 || Rate models || 3.3,3.4 || |
|- | |- | ||
| Feb 2 || Plasticity 1: associators and physiology || 4.1,4.2, [[Media:Chapter4.pdf| slides 4]] | | Feb 2 || Plasticity 1: associators and physiology || 4.1,4.2, [[Media:Chapter4.pdf| slides 4]] | ||
|- | |- | ||
| − | | Feb 4 || Plasticity 2: Mathematical descriptions || 4.3,4.4 || | + | | Feb 4 || Plasticity 2: Mathematical descriptions || 4.3,4.4 || |
| − | |||
| Feb 9 || Networks 1: Background || 5.1,5.2, [[Media:Chapter5.pdf| slides 5]] | | Feb 9 || Networks 1: Background || 5.1,5.2, [[Media:Chapter5.pdf| slides 5]] | ||
|- | |- | ||
| − | | Feb 11 || Network of Izhikevich neurons || 5.3 || | + | | Feb 11 || Network of Izhikevich neurons || 5.3 || |
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| Feb 23 || Multilayer Perceptron 1 || 6.1, 6.2, [[Media:Chapter6.pdf| slides 6]] || | | Feb 23 || Multilayer Perceptron 1 || 6.1, 6.2, [[Media:Chapter6.pdf| slides 6]] || | ||
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| − | | Feb 25 || Multilayer Perceptron 2 || (6.3,6.4) or application || | + | | Feb 25 || Multilayer Perceptron 2 || (6.3,6.4) or application || |
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| March 1 || PPP (Phenomenal Perceptron Project) | | March 1 || PPP (Phenomenal Perceptron Project) | ||
| Line 59: | Line 58: | ||
| March 10 || PPP (Phenomenal Perceptron Project) | | March 10 || PPP (Phenomenal Perceptron Project) | ||
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| − | | March 15 || Self-Organizing Maps || 7.1,7.2 | + | | March 15 || Self-Organizing Maps || 7.1,7.2 |
|- | |- | ||
| − | | March 17 || Attractor Networks || 8.1-8.2 [[Media:Chapter8.pdf| slides 8]] || | + | | March 17 || Attractor Networks || 8.1-8.2 [[Media:Chapter8.pdf| slides 8]] || |
|- | |- | ||
| March 22 || Reinforcement learning || 9.6 [[Media:Chapter9.pdf| slides 9]] | | March 22 || Reinforcement learning || 9.6 [[Media:Chapter9.pdf| slides 9]] | ||
|- | |- | ||
| − | | March 24 || Reinforcement learning || 9.6 || | + | | March 24 || Reinforcement learning || 9.6 || |
|- | |- | ||
| March 29 || Cognitive Brain 1: Competitive dynamics and dynamic networks || 10.1,10.2, [[Media:Chapter10.pdf| slides 10]] | | March 29 || Cognitive Brain 1: Competitive dynamics and dynamic networks || 10.1,10.2, [[Media:Chapter10.pdf| slides 10]] | ||
Revision as of 17:10, 10 December 2015
Contents
Neural Computation / Theoretical Neuroscience 2016
Instructor
Dr. Thomas Trappenberg
Office: Room 4216 in Mona Campbell Building
Email: tt@cs.dal.ca
TA: Paul Hollensen <paulhollensen@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
Schedule (tentative, will change)
| Date | Content | Reference | Assignment | |||
|---|---|---|---|---|---|---|
| Jan 5 | Overview | Chapter 1, slides 1 | ||||
| Jan 7 | Neuron1: Overview and synaptic transmission | 2.1, 2.2, slides 2 | ||||
| Jan 12 | MATLAB 1: General programming | Appendix E | ||||
| Jan14 | Basic Calculus | Appendix B | ||||
| Jan 19 | (Paul) MATLAB 2: ODE | Appendix E and B | ||||
| Jan 21 | Neuron 2: Axon and conductance-based compartmental models | 2.3,2.4 | ||||
| Jan 26 | Spiking models | 3.1,3.2, slides 3 | ||||
| Jan 28 | Rate models | 3.3,3.4 | ||||
| Feb 2 | Plasticity 1: associators and physiology | 4.1,4.2, slides 4 | ||||
| Feb 4 | Plasticity 2: Mathematical descriptions | 4.3,4.4 | Feb 9 | Networks 1: Background | 5.1,5.2, slides 5 | |
| Feb 11 | Network of Izhikevich neurons | 5.3 | ||||
| Feb 23 | Multilayer Perceptron 1 | 6.1, 6.2, slides 6 | ||||
| Feb 25 | Multilayer Perceptron 2 | (6.3,6.4) or application | ||||
| March 1 | PPP (Phenomenal Perceptron Project) | |||||
| March 3 | PPP (Phenomenal Perceptron Project) | |||||
| March 8 | PPP (Phenomenal Perceptron Project) | |||||
| March 10 | PPP (Phenomenal Perceptron Project) | |||||
| March 15 | Self-Organizing Maps | 7.1,7.2 | ||||
| March 17 | Attractor Networks | 8.1-8.2 slides 8 | ||||
| March 22 | Reinforcement learning | 9.6 slides 9 | ||||
| March 24 | Reinforcement learning | 9.6 | ||||
| March 29 | Cognitive Brain 1: Competitive dynamics and dynamic networks | 10.1,10.2, slides 10 | ||||
| March 31 | Cognitive Brain 2: The anticipating brain | 10.3,10.4 | ||||
| April 5 | TBA |
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
Grading Scheme
Projects 75%, Midterm 10%, Final 15%
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.
