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| YZLM5190 | Principles of Brain Computation | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of SOFTWARE ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Eyüp GEDİKLİ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course provides an introduction to Computational Neuroscience, and also into related engineering disciplines. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Interpret human learning skills | 1,4 | | | PO - 2 : | Model artificial neural systems | 1,4 | | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| The Hodgkin-Huxley Model,Dendrites and Synapses, Dimensionality Reduction and Phase Plane Analysis, Nonlinear Integrate-and-Fire Models, Adaptation Patterns, Variability of Neural Codes, Noisy Input Models: Barrage of Spike Arrivals, Noisy Output: Escape Rate and Soft Threshold, Estimating Models, Encoding and Decoding with Stochastic Neuron models, Neuronal Populations, Continuity Equation and the Fokker-Planck Approach,The Integral-equation Approach, Fast Transients and Rate Models, Competing Populations and Decision Making, Memory Dynamics,Cortical Field Models for Perception,Synaptic Plasticity and Learning, Dynamics in Plastic Networks. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Foundations of Neuronal Dynamics; Introduction,The Hodgkin-Huxley Model | | | Week 2 | Foundations of Neuronal Dynamics; Dendrites and Synapses, Dimensionality Reduction and Phase Plane Analysis | | | Week 3 | Generalized Integrate-and-Fire Neurons; Nonlinear Integrate-and-Fire Models, Adaptation and Firing Patterns | | | Week 4 | Generalized Integrate-and-Fire Neurons; Variability of Spike Trains and Neural Codes | | | Week 5 | Generalized Integrate-and-Fire Neurons; Noisy Input Models: Barrage of Spike Arrivals, Noisy Output: Escape Rate and Soft Threshold | | | Week 6 | Generalized Integrate-and-Fire Neurons; Estimating Models, Encoding and Decoding with Stochastic Neuron models | | | Week 7 | Networks of Neurons and Population Activity; Neuronal Populations | | | Week 8 | Networks of Neurons and Population Activity; Continuity Equation and the Fokker-Planck Approach | | | Week 9 | Mid-term exam | | | Week 10 | Networks of Neurons and Population Activity;The Integral-equation Approach, Fast Transients and Rate Models | | | Week 11 | Dynamics of Cognition; Competing Populations and Decision Making | | | Week 12 | Dynamics of Cognition; Memory and Attractor Dynamics | | | Week 13 | Dynamics of Cognition; Cortical Field Models for Perception | | | Week 14 | Dynamics of Cognition; Synaptic Plasticity and Learning | | | Week 15 | Dynamics of Cognition; Outlook: Dynamics in Plastic Networks | | | Week 16 | Final exam | | | |
| 1 | Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski. Neuronal Dynamics. From single neurons to networks and models of cognition. Available online | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | In-term studies (second mid-term exam) | 9 | 01.04.2022 | 2 | 50 | | End-of-term exam | 15
16 | 01.05.2022 | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 8 | 14 | 112 | | Arasınav için hazırlık | 5 | 1 | 5 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 10 | 2 | 20 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 183 |
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