BLG 643 - Neuromorphic Computing

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.