Seminar From Biological to Artificial Neural Networks
Artificial neural networks were not only inspired by the brain, but were created in an effort to understand and model the functioning of the brain. In this seminar, we will read and discuss historic scientific articles that track the development of neural networks from the 1940s to the present. Specific topics include
McCulloch-Pitts Neurons/ Boolean networks
Hubel and Wiesel
Convolutional Neural Networks
After successful completion of this seminar, students will be able to
read and understand scientific articles in neural network research
know in which situations neural networks are applied
understand and discuss the advantages and disadvantages of specific neural networks
understand the historical development of neural networks
present the results of research in neural networks to an audience
Prof. Dr. Sen ChengLecturer
|(+49) 234-32-29486 email@example.com NB 3/33|
- Course type
- Summer Term 2023
every week on Tuesday from 10:00 to 12:00 in room NB 3/57.
First appointment is on 04.04.2023
Last appointment is on 11.07.2023
Solid knowledge of calculus, linear algebra, and statistics are required, e.g. Mathematik 1 und 2, Statistik. Knowledge of artificial neural networks.
Students should have taken the class “ A rtificial Neural Networks” , or something equivalent, before enrolling in this seminar.
The articles will be announced in the first meeting.
Background reading: “Neural Networks and Deep Learning” by Charu C. Aggarwal, Springer
The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210