| Christoph von der Malsburg | |
| Birth Date: | 8 May 1942 |
| Birth Place: | Kassel, Hesse-Nassau |
| Nationality: | German |
| Fields: | Neuroscientist |
| Workplaces: | University of Southern California |
| Alma Mater: | University of Heidelberg |
| Thesis Title: | Bestimmung der π−-Masse |
| Thesis Url: | https://katalog.ub.uni-heidelberg.de/titel/67152672 |
| Thesis Year: | 1970 |
| Doctoral Advisors: | )--> |
| Doctoral Students: | Ladan Shams |
Christoph von der Malsburg (born 8 May 1942) is a German physicist and neuroscientist.
von der Malsburg obtained his PhD with a concentration in elementary particle physics at CERN and the University of Heidelberg in 1970.
He joined the neurobiology department of the Max-Planck-Institute for Biophysical Chemistry in Göttingen. He remained there until 1987, when he became a professor of Computer Science, Neuroscience, Physics and Psychology at the University of Southern California in Los Angeles.[1] In 1990, he founded the Institut für Neuroinformatik (Institute for Neural Computation) at Ruhr-University Bochum, together with Werner von Seelen. He commuted between the two institutions until 2007, when he joined the Frankfurt Institute for Advanced Studies as a Senior Fellow.[2]
Von der Malsburg co-founded two companies and received a number of awards. He is a member of the scientific advisory board of the Human Brain Project[3] and one of the members of the founding board of Mindfire Foundation, of which he also heads the neuroscience board.[4]
His research interests focused on processes of organization in the brain with emphasis on the structure and function of the visual system. His publications concerning the theory of self-organization of regular fiber projections in the visual system made him a pioneer of this field. He is known for his criticism of the theory of neural networks.[5] He claimed that the generally accepted view fails because of the binding problem. To solve it he formulated the Dynamic Link Architecture, a system of rapidly switching network fragments, as applied to facial recognition.[6] [7]