Master thesis

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Error backpropagation in neuromorphic spiking networks
Learning with backpropagation in artificial neural networks is the key behind most of the recent success stories in artificial intelligence. This has spurred the investigation of its compatibility with neocortical learning and development. At the same time, spiking neuromorphic chips offer a fast and energy efficient substrate for such brain-inspired computing. Still, the adaptation of backpropagation to this novel hardware remains a challenge due to the particular dynamics of spiking neurons and the requirement that the calculation of gradients should happen in a local manner. This Masterís project is concerned with the spike-based formulation of a recently developed model of backpropagation in biological neural networks and its adaptation to the spiking neuromorphic systems developed in the Electronic Vision(s) group. A successful project will unite prospective advantages of the novel hardware and the proven strength of backpropagation.
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