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000873629 0247_ $$2arXiv$$aarXiv:1905.12116
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000873629 037__ $$aFZJ-2020-00872
000873629 1001_ $$0P:(DE-HGF)0$$aLiu, Tianlin$$b0$$eCorresponding author
000873629 245__ $$aHarnessing Slow Dynamics in Neuromorphic Computation$$f - 2019-05-30
000873629 260__ $$c2019
000873629 300__ $$a53 p.
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000873629 500__ $$aMaster thesis of Tianlin LiuLiu was supported by the FZJ through the project SMARTSTART Computational Neuroscience, DB001423.
000873629 502__ $$aMasterarbeit, Jacobs University Bremen, 2019$$bMasterarbeit$$cJacobs University Bremen$$d2019
000873629 520__ $$aNeuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neural networks deployed on these systems is challenging. One main challenge is the so-called timescale mismatch: Dynamics of analog circuits tend to be too fast to process real-time sensory inputs. In this thesis, we propose a few working solutions to slow down dynamics of on-chip spiking neural networks. We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks.
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