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Course Information

Course Name
Turkish Bilgisayar Mühendisliğinde Gelişmiş Konular
English Advanced Topics in Comp. Eng.
Course Code
BLG 609E Credit Lecture
(hour/week)
Recitation
(hour/week)
Laboratory
(hour/week)
Semester -
3 3 - -
Course Language English
Course Coordinator Burak Berk Üstündağ
Course Objectives 1- Understanding neuromorphic computational paradigms of intelligent systems in nature
2- Understanding and performance measure of information processing in neuromorphic systems
3- Design of Spiking Neural Networks depending on neuron and neural system models
4- Design of neuromorphic systems for computation and machine learning
5- Implementation methods of neuromorphic information processing systems (Memristors,BNNs etc.)
Course Description Due to development of AI applications, performing the cognitive functions by using nature inspired computational paradigms has become a major trend for increasing the power and data efficiency. Neuromorphic computing is based on investigation, modeling and emulation of biological neural systems and the brain-connectome structure. This course covers neuromorphic learning in brain-nerve-connectome structures, spiking neural networks and their artificial implementation, neuromorphic coding, basics of stochastic computing, biomimetic neural networks, cognitive functions and neuromorphic system applications.
Course Outcomes M.Sc./Ph.D. students who successfully pass this course gain knowledge, skill and competency in the following subjects;
1- Understanding the requirements for changing the Von Neumann architecture depending on analogy between the neuromorphic systems in nature and the brain inspired artificial intelligence.
2- Understanding the reasoning, context awareness, situational awareness, and intelligence in cognitive systems and their test methods.
3- Modeling of stochastic coding relationship between structural formation and the processed signals in neuromorphic systems
4-Learning the basics of mathematical operations used in investigation and emulation of neuromorphic systems.
5-Understanding the neuron models and spiking neural networks (SNN)
1- Application of spiking neural networks (SNN) for computation and machine learning.
2- Application of Minimum Energy-Maximum Entropy principle in neuromorphic computing
3- Learning the recent physical implementation methods for neuromorphic computing (memristors etc.)
Pre-requisite(s)
Required Facilities
Other
Textbook N.K. Kasabov, “Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence”, Springer, 2019
Other References Q. Yu, H. Tang, J. Hu, K.T. Chen, “Neuromorphic Cognitive Systems: A Learning and Memory Centered Approach”, Springer, 2017
M.Shahsavari, “Unconventional Computation From Digital to Brain-like Neuromorphic: Memristive Computing”, Springer, 2017
M.Suri, “Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices”, Springer, 2017
W.J.Gross, V.C.Gaduet “Stochastic Computing: Techniques and Applications”, Springer, 2019
 
 
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