Johannes Schemmel

Affiliations: 
Department of Physics Ruprecht Karls University Heidelberg, Heidelberg, Baden-Württemberg, Germany 
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Cramer B, Stöckel D, Kreft M, et al. (2020) Control of criticality and computation in spiking neuromorphic networks with plasticity. Nature Communications. 11: 2853
Kungl AF, Schmitt S, Klähn J, et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Frontiers in Neuroscience. 13: 1201
Jordan J, Petrovici MA, Breitwieser O, et al. (2019) Deterministic networks for probabilistic computing. Scientific Reports. 9: 18303
Dold D, Bytschok I, Kungl AF, et al. (2019) Stochasticity from function - Why the Bayesian brain may need no noise. Neural Networks : the Official Journal of the International Neural Network Society. 119: 200-213
Wunderlich T, Kungl AF, Müller E, et al. (2019) Demonstrating Advantages of Neuromorphic Computation: A Pilot Study. Frontiers in Neuroscience. 13: 260
Aamir SA, Muller P, Kiene G, et al. (2018) A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores. Ieee Transactions On Biomedical Circuits and Systems
Leng L, Martel R, Breitwieser O, et al. (2018) Spiking neurons with short-term synaptic plasticity form superior generative networks. Scientific Reports. 8: 10651
Friedmann S, Schemmel J, Grubl A, et al. (2017) Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System. Ieee Transactions On Biomedical Circuits and Systems. 11: 128-142
Petrovici MA, Bill J, Bytschok I, et al. (2016) Stochastic inference with spiking neurons in the high-conductance state. Physical Review. E. 94: 042312
Probst D, Petrovici MA, Bytschok I, et al. (2015) Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons. Frontiers in Computational Neuroscience. 9: 13
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