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pro vyhledávání: '"Engelken, Rainer"'
Autor:
Engelken, Rainer
Training recurrent neural networks (RNNs) remains a challenge due to the instability of gradients across long time horizons, which can lead to exploding and vanishing gradients. Recent research has linked these problems to the values of Lyapunov expo
Externí odkaz:
http://arxiv.org/abs/2312.17306
Autor:
Engelken, Rainer
Spiking Neural Networks (SNNs) are biologically-inspired models that are capable of processing information in streams of action potentials. However, simulating and training SNNs is computationally expensive due to the need to solve large systems of c
Externí odkaz:
http://arxiv.org/abs/2312.17216
Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that
Externí odkaz:
http://arxiv.org/abs/2201.09916
Brains process information through the collective dynamics of large neural networks. Collective chaos was suggested to underlie the complex ongoing dynamics observed in cerebral cortical circuits and determine the impact and processing of incoming in
Externí odkaz:
http://arxiv.org/abs/2006.02427
Akademický článek
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Autor:
Wolf, Fred, Engelken, Rainer, Puelma-Touzel, Maximilian, Weidinger, Juan Daniel Flórez, Neef, Andreas
Publikováno v:
In Current Opinion in Neurobiology April 2014 25:228-236
Autor:
Engelken, Rainer, Goedeke, Sven
Bernstein Conference 2022 abstract. http://bernstein-conference.de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a10141e3a0f6801d8f80c4a8c457548
Bernstein Conference 2022 abstract. http://bernstein-conference.de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff6d402f361ea538c61b88fbc3033a9b
Publikováno v:
PLoS Computational Biology; 12/5/2022, Vol. 18 Issue 12, p1-23, 23p, 1 Chart, 8 Graphs