The brain is characterized by extreme power efficiency, fault tolerance, compactness and the ability to develop and to learn. It can make predictions from noisy and unexpected input data. Any artificial system implementing all or some of those features is likely to have a large impact on the way we process information.
With the increasingly detailed data from neuroscience and the availability of advanced VLSI process nodes the dream of building physical models of neural circuits on a meaningful scale of complexity is coming closer to realization. Such models deviate strongly from classical processor-memory based numerical machines as the two functions merge into a massively parallel network of almost identical cells.
The lecture will introduce current projects worldwide and the approach proposed by the EU Human Brain Project to establish a systematic path from biological data, simulations on supercomputers and systematic reduction of cell complexity to derived neuromorphic hardware implementations with a very high degree of configurability.
Karlheinz Meier is a professor of experimental physics at Heidelberg University in Germany. He received his PhD in 1984 from Hamburg University. For more than 30 years he worked in experimental particle physics, contributing to several experiments at the CERN and DESY laboratories. He designed and implemented a large-scale data selection system for an LHC experiment at CERN: Since 2005 he has shifted his interest towards custom hardware implementations of neural circuits. He has initiated and led 2 major European initiatives in the field (FACETS and BrainScaleS) and is currently co-director of the Human Brain Project.