Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| NEURAL NETWORKS | SSMY1264090 | Spring Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Mehmet KOCATÜRK |
| Name of Lecturer(s) | Assist.Prof. Mehmet KOCATÜRK |
| Assistant(s) | |
| Aim | The objective of the course is to evaluate the information processing techniques and control algorithms based on utilization of computational neurons. |
| Course Content | This course contains; The Nervous System: Microscopic View,The Nervous System: Macroscopic View,Perceptron,Multilayer Perceptron,Supervised Learning,Hodgkin-Huxley Model,Izhikevich Model,Synaptic Interaction Models,Neuromodulation – Reinforcement Learning,Spike Timing - Oscillations,Spiking Neural Network Simulation,Real-time Spiking Neural Network Simulation,Neuromorphic Processors,Large-Scale Neuronal Network Modeling. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Describes and evaluates fundamental neuron and synaptics interaction models. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Explains key concepts in neuronal coding. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Explains dynamics of biological neuronal circuits using mathematical modeling techniques. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Identifies hardware and software tools for neuronal network simulations. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Creates artificial neural networks to resolve engineering problems. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | The Nervous System: Microscopic View | Week 1 lecture notes. |
| 2 | The Nervous System: Macroscopic View | Week 1 lecture notes (continued). |
| 3 | Perceptron | Week 3 lecture notes. |
| 4 | Multilayer Perceptron | Week 4 lecture notes. |
| 5 | Supervised Learning | Week 5 lecture notes. |
| 6 | Hodgkin-Huxley Model | Week 6 lecture notes. |
| 7 | Izhikevich Model | Week 7 lecture notes. |
| 8 | Synaptic Interaction Models | Week 8 lecture notes. |
| 9 | Neuromodulation – Reinforcement Learning | Week 9 lecture notes. |
| 10 | Spike Timing - Oscillations | Week 10 lecture notes. |
| 11 | Spiking Neural Network Simulation | Week 11 lecture notes. |
| 12 | Real-time Spiking Neural Network Simulation | Week 12 lecture notes. |
| 13 | Neuromorphic Processors | Week 13 lecture notes. |
| 14 | Large-Scale Neuronal Network Modeling | Week 14 lecture notes. |
| Resources |
| Alpaydin, E., (2010) Introduction to machine learning, MIT Press,Cambridge. Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience, Springer, New York. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | X | |||||
| 5 | Independently conduct studies that require proficiency in the field. | ||||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | ||||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | X | |||||
| 9 | Define the social and environmental aspects of engineering applications. | ||||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 10 | 12 | 120 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 30 | 30 | |||
| General Exam | 1 | 40 | 40 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 232 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(232/30) | 8 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| NEURAL NETWORKS | SSMY1264090 | Spring Semester | 3+0 | 3 | 8 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | Turkish |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Mehmet KOCATÜRK |
| Name of Lecturer(s) | Assist.Prof. Mehmet KOCATÜRK |
| Assistant(s) | |
| Aim | The objective of the course is to evaluate the information processing techniques and control algorithms based on utilization of computational neurons. |
| Course Content | This course contains; The Nervous System: Microscopic View,The Nervous System: Macroscopic View,Perceptron,Multilayer Perceptron,Supervised Learning,Hodgkin-Huxley Model,Izhikevich Model,Synaptic Interaction Models,Neuromodulation – Reinforcement Learning,Spike Timing - Oscillations,Spiking Neural Network Simulation,Real-time Spiking Neural Network Simulation,Neuromorphic Processors,Large-Scale Neuronal Network Modeling. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Describes and evaluates fundamental neuron and synaptics interaction models. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Explains key concepts in neuronal coding. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Explains dynamics of biological neuronal circuits using mathematical modeling techniques. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Identifies hardware and software tools for neuronal network simulations. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Creates artificial neural networks to resolve engineering problems. | 10, 12, 14, 16, 19, 2, 21, 6, 9 | A, E, F |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | The Nervous System: Microscopic View | Week 1 lecture notes. |
| 2 | The Nervous System: Macroscopic View | Week 1 lecture notes (continued). |
| 3 | Perceptron | Week 3 lecture notes. |
| 4 | Multilayer Perceptron | Week 4 lecture notes. |
| 5 | Supervised Learning | Week 5 lecture notes. |
| 6 | Hodgkin-Huxley Model | Week 6 lecture notes. |
| 7 | Izhikevich Model | Week 7 lecture notes. |
| 8 | Synaptic Interaction Models | Week 8 lecture notes. |
| 9 | Neuromodulation – Reinforcement Learning | Week 9 lecture notes. |
| 10 | Spike Timing - Oscillations | Week 10 lecture notes. |
| 11 | Spiking Neural Network Simulation | Week 11 lecture notes. |
| 12 | Real-time Spiking Neural Network Simulation | Week 12 lecture notes. |
| 13 | Neuromorphic Processors | Week 13 lecture notes. |
| 14 | Large-Scale Neuronal Network Modeling | Week 14 lecture notes. |
| Resources |
| Alpaydin, E., (2010) Introduction to machine learning, MIT Press,Cambridge. Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience, Springer, New York. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | X | |||||
| 5 | Independently conduct studies that require proficiency in the field. | ||||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | ||||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | X | |||||
| 9 | Define the social and environmental aspects of engineering applications. | ||||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |