Simon Friedmann

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We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a(More)
In this study, we propose and analyze in simulations a new, highly flexible method of implementing synaptic plasticity in a wafer-scale, accelerated neuromorphic hardware system. The study focuses on globally modulated STDP, as a special use-case of this method. Flexibility is achieved by embedding a general-purpose processor dedicated to plasticity into(More)
In this paper we present how adaptable learned models of graphical models are and how they can be used for classification tasks of 3D laser point clouds with different distributions and density. In order to model the contextual information we use a pair-wise conditional random field and an adaptive graph down-sampling method based on voxel grids. As feature(More)
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