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Course Weekly Lecture Plan

Week Topic
1 Von Neumann bottleneck: Machine learning with conventional computation vs neuromorphic computation
Cognitive Neuromorphic systems in nature:“An introduction to Brain Connectome and Neural Systems”
Mathematical methods for modeling and computation in neuromorphic systems-part 1: Real time convolution, Kullback-Liebler Entropy and an introduction to stochastic modeling
Mathematical methods for modeling and computation in neuromorphic systems-part 2: Dalgacık Dönüşümü (DWT) ve uygulama örnekleri
Mathematical methods for modeling and computation in neuromorphic systems-part 3: Filtering, noise models, frequency space analysis and Hilbert transformation
Information coding and cortical network development based on the convolution and entropy
Context awareness, situational awareness and reasoning in neuromorphic systems
Peripheral physical interactions through motor cortex in neuromorphic systems and articifial motor cortex examples
Neuron models and spiking neural networks (SNN) in brain
Computation and machine learning with artificial spiking neural networks (SNN)
Implementation examples of spiking neural networks (SNN) for machine learning
Recent physical implementation methods of neuromorphic computational systems (Memristors, biomimetic networks etc.) and their applications for artificial intelligence
1st group term project presentations and discussion: Computation and machine learning in neuromorphic systems
2nd group term project presentations and discussion: Neuromorphic system implementation and analysis
 
 
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