Syllabus ( BENG 426 )
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Basic information
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| Course title: |
Theoretical Neuroscience |
| Course code: |
BENG 426 |
| Lecturer: |
Prof. Dr. Muhammet UZUNTARLA
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| ECTS credits: |
5 |
| GTU credits: |
3 () |
| Year, Semester: |
4, Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Departmental Elective
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| Language of instruction: |
English
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
Yok |
| Professional practice: |
No |
| Purpose of the course: |
This course aims to present theoretical information on neuroscience including physical principles and mathematical models. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Define the dynamics of neurological systems.
Contribution to Program Outcomes
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Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
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Apply mathematical analysis and modeling methods for bioengineering design and production processes.
Method of assessment
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Written exam
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Model the artificial neurological systems
Contribution to Program Outcomes
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Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,
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Apply mathematical analysis and modeling methods for bioengineering design and production processes.
Method of assessment
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Written exam
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Relate the models of learning to neuroscience.
Contribution to Program Outcomes
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Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,
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Find out new methods to improve his/her knowledge.
Method of assessment
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Written exam
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Contents
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| Week 1: |
Ion flux in membranes and Nernst Planck Equation |
| Week 2: |
Ion-Channels, Excitable membranes, |
| Week 3: |
Spiking, Hodgkin Huxley models
Homework 1 |
| Week 4: |
Integrate and Fire Neurons |
| Week 5: |
Neural Encoding and Decoding
Homework 2 |
| Week 6: |
Spike train statistics |
| Week 7: |
Aksiyon potansiyel istatistikleri
Midterm Exam |
| Week 8: |
Applications of Information Theory in neural coding and decoding |
| Week 9: |
Plasticity: Adaptation and Learning
Homework 3 |
| Week 10: |
Synapses: structure and function, plasticity |
| Week 11: |
Spike Timing Dependent Plasticity (STDP)
Homework 4 |
| Week 12: |
Learning rules, Supervised and Unsupervised Learning |
| Week 13: |
Classical conditioning |
| Week 14: |
Reinforcement Learning |
| Week 15*: |
- |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski, (2014) Neuronal Dynamics From Single Neurons to Networks and Models of Cognition, Cambridge University Press.
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| Recommended readings: |
Dayan P., Abbott L. F., 2001, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
7 |
40 |
| Other in-term studies: |
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0 |
| Project: |
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0 |
| Homework: |
3,5,9,11 |
20 |
| Quiz: |
- |
0 |
| Final exam: |
16 |
40 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
14 |
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| Own studies outside class: |
1 |
14 |
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| Practice, Recitation: |
0 |
0 |
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| Homework: |
6 |
4 |
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| Term project: |
0 |
0 |
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| Term project presentation: |
0 |
0 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
3 |
7 |
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| Mid-term: |
3 |
1 |
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| Personal studies for final exam: |
3 |
7 |
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| Final exam: |
3 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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