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| Hauptseite > Publikationsdatenbank > Unfolding recurrence by Green’s functions for optimized reservoir computing > print |
| Hauptseite > Publikationsdatenbank > Unfolding recurrence by Green’s functions for optimized reservoir computing > print |
| 001 | 892802 | ||
| 005 | 20240313095023.0 | ||
| 037 | _ | _ | |a FZJ-2021-02359 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Nestler, Sandra |0 P:(DE-Juel1)174585 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a RNN seminar talk (online) |c Mortimer B. Zuckerman Mind Brain Behavior Institute |w USA |
| 245 | _ | _ | |a Unfolding recurrence by Green’s functions for optimized reservoir computing |f 2021-05-12 - |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
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| 520 | _ | _ | |a Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the application of feed-forward networks and their theoretical understanding, it remains unclear how the interplay of recurrence and non-linearities in recurrent cortical networks contributes to their function. The purpose of this work is to present a solvable recurrent network model that links to feed forward networks. By perturbative methods we transform the time-continuous,recurrent dynamics into an effective feed-forward structure of linear and non-linear temporal kernels. The resulting analytical expressions allow us to build optimal time-series classifiers from random reservoir networks. Firstly, this allows us to optimize not only the readout vectors, but also the input projection, demonstrating a strong potential performance gain. Secondly, the analysis exposes how the second order stimulus statistics is a crucial element that interacts with the non-linearity of the dynamics and boosts performance. |
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| 700 | 1 | _ | |a Keup, Christian |0 P:(DE-Juel1)171384 |b 1 |u fzj |
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