Seminarium wydziałowe 18marca 2026 r. o godz. 12:00 w sali A134
12-03-2026
Szanowni Państwo,
serdecznie zapraszamy na seminarium wydziałowe, które odbędzie się 18.03 o godzinie 12:00 w sali A134.
Prelegent: Assoc. Prof. Dr. Meriç ÇETİN
PAMUKKALE UNIVERSITY
Computer Engineering Department
Kınıklı Campus, Denizli-TURKEY
Tytuł wystąpienia:
Computational Neuroscience Applications from Computer Engineering Perspective
Computational Neuroscience Applications from Computer Engineering Perspective
Abstract:
Computational neuroscience, primarily encompassing brain research, examines the structure, function and disorders of the nervous system, as well as the mathematical modeling of biological nervous systems and artificial intelligence-based designs. Within this scope, fundamental neuroscience concepts such as brain physiology, neuron structure and synapses will be evaluated using engineering-based modeling approaches. Conduction-based dynamic neuron models such as Hodgkin–Huxley, FitzHugh–Nagumo, and Hindmarsh–Rose will be examined within the framework of nonlinear differential equations, demonstrating how they represent dynamic properties such as chaotic behavior. Furthermore, observer design and predictive modeling approaches for these dynamics will be discussed in the context of state estimation and control applications. The role of intelligent methods such as machine learning techniques and neural mechanism-based architectures (artificial neural networks, deep learning, etc.) in the analysis of neuroscience data and brain-based model development processes will be evaluated. Neuroscience-centered datasets and the devices and equipment used in collecting this data will be introduced. Literature studies will be presented.
Computational neuroscience, primarily encompassing brain research, examines the structure, function and disorders of the nervous system, as well as the mathematical modeling of biological nervous systems and artificial intelligence-based designs. Within this scope, fundamental neuroscience concepts such as brain physiology, neuron structure and synapses will be evaluated using engineering-based modeling approaches. Conduction-based dynamic neuron models such as Hodgkin–Huxley, FitzHugh–Nagumo, and Hindmarsh–Rose will be examined within the framework of nonlinear differential equations, demonstrating how they represent dynamic properties such as chaotic behavior. Furthermore, observer design and predictive modeling approaches for these dynamics will be discussed in the context of state estimation and control applications. The role of intelligent methods such as machine learning techniques and neural mechanism-based architectures (artificial neural networks, deep learning, etc.) in the analysis of neuroscience data and brain-based model development processes will be evaluated. Neuroscience-centered datasets and the devices and equipment used in collecting this data will be introduced. Literature studies will be presented.