Wolfgang Maass - Short Scientific Biography
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After studying mathematics at the Ludwig Maximilians Universität in Munich, he received there his Phd in 1974. His thesis advisor was Prof. Dr. Kurt Schütte. The topic of his thesis were applications of lambda calculus in proof theory. Wolfgang Maass then worked on further open problems of computability in the context of Mathematical Logic, and discovered in particular the concept and role of promptly simple sets, which spurned quite a bit of subsequent research in computability theory. He received his Habilitation in Mathematics at the Ludwigs Maximilians Universität in Munich in 1978.

Around 1983 he started to work also on Computational Complexity Theory (within the context of Theoretical Computer Science). He wrote for example with D. Hochbaum in 1985 the article “Approximation schemes for covering and packing problems in image processing and VLSI” , which has been cited 491 times. He then focused on the computational complexity theory of neural networks and learning theory. In particular he co-authored in 1993 the article “Threshold circuits of bounded depth”, which has been cited 296 times.

Around 1995 he became interested in data-based models for networks of neurons in the brain, especially networks of spiking neurons, and has worked since then on the analysis of computing- and learning capabilities of these networks. His main goal became to build bridges between computability theory in mathematics and computer science on one hand, and computations in networks of neurons in the brain on the other hand. He soon realized, that for that purpose direct collaboration with experimental neuroscientists is essential. For example, he wrote in 2002 together with the neuroscientist Henry Markram the article “Real-time computing without stable states: A new framework for neural computation based on perturbations”. This paper presented one of the first computational models for data-based models of cortical microcircuits (the liquid computing model), and has been cited 996 times. Since then he has also co-authored papers with several other experimental neuroscientists, such as Wolf Singer, Rodney Douglas, Nikos Logothetis, Andrew Schwartz, and Shihab Shamma. In particular he tested in collaboration with the Labs of Wolf Singer and Shihab Shamma predictions of the liquid computing model. Other publications address computational models for cortical microcircuits, learning in the context of synaptic plasticity and neural network learning, and applications of learning algorithms in robotics. Recently he has established in collaboration with the Lab of Rolf Pfeifer (Univ. Zürich) a theoretical foundation and new paradigms for the concept of morphological computation in robotics.
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