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Autor:
Schneider, Andreas C., Neuhaus, Valentin, Ehrlich, David A., Makkeh, Abdullah, Ecker, Alexander S., Priesemann, Viola, Wibral, Michael
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and ef
Externí odkaz:
http://arxiv.org/abs/2412.02482
Autor:
Makkeh, Abdullah, Graetz, Marcel, Schneider, Andreas C., Ehrlich, David A., Priesemann, Viola, Wibral, Michael
Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions remains a challenge to this date. Efforts to bring learning to a more
Externí odkaz:
http://arxiv.org/abs/2306.02149
Autor:
Ehrlich, David A., Schneider, Andreas C., Priesemann, Viola, Wibral, Michael, Makkeh, Abdullah
Publikováno v:
Transactions on Machine Learning Research (TMLR), 05/2023
In neural networks, task-relevant information is represented jointly by groups of neurons. However, the specific way in which this mutual information about the classification label is distributed among the individual neurons is not well understood: W
Externí odkaz:
http://arxiv.org/abs/2209.10438
Autor:
Schneider, Andreas N., Castro, David, Holmlund, Mattias, Näsholm, Torgny, Hurry, Vaughan, Street, Nathaniel R.
Publikováno v:
In Trees, Forests and People June 2024 16
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