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000873632 0247_ $$2doi$$a10.1007/978-3-030-30487-4_54
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000873632 0247_ $$2ISSN$$a1611-3349
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000873632 037__ $$aFZJ-2020-00875
000873632 1001_ $$00000-0002-6855-0012$$aTetko, Igor V.$$b0$$eEditor
000873632 1112_ $$aICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation pp$$cMunich$$d2019-09-17 - 2019-09-19$$wGermany
000873632 245__ $$aTraining Delays in Spiking Neural Networks
000873632 260__ $$c2019
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000873632 520__ $$aSpiking Neural Networks (SNNs) are a promising computational paradigm, both to understand biological information processing and for low-power, embedded chips. Although SNNs are known to encode information in the precise timing of spikes, conventional artificial learning algorithms do not take this into account directly. In this work, we implement the spike timing by training the synaptic delays in a single layer SNN. We use two different approaches: a classical gradient descent and a direct algebraic method that is based on a complex-valued encoding of the spikes. Both algorithms are equally able to correctly solve simple detection tasks. Our work provides new optimization methods for the data analysis of highly time-dependent data and training methods for neuromorphic chips.
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000873632 536__ $$0G:(EU-Grant)90251$$aSmartstart - SMARTSTART Training Program in Computational Neuroscience (90251)$$c90251$$x1
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000873632 7001_ $$00000-0002-8181-2128$$aKůrková, Věra$$b1$$eEditor
000873632 7001_ $$00000-0003-4786-9806$$aKarpov, Pavel$$b2$$eEditor
000873632 7001_ $$00000-0002-2419-1943$$aTheis, Fabian$$b3$$eEditor
000873632 7001_ $$00000-0001-8084-5297$$aState, Laura$$b4
000873632 7001_ $$00000-0002-1218-4009$$aVilimelis Aceituno, Pau$$b5$$eCorresponding author
000873632 773__ $$a10.1007/978-3-030-30487-4_54
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000873632 9141_ $$y2020
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000873632 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000873632 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000873632 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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