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| Hauptseite > Publikationsdatenbank > Finding new bio-plausible Learning Rules using Deep Reinforcement Learning > print |
| Hauptseite > Publikationsdatenbank > Finding new bio-plausible Learning Rules using Deep Reinforcement Learning > print |
| 001 | 1021872 | ||
| 005 | 20250203103350.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01020 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01020 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Lohoff, Jamie |0 P:(DE-Juel1)192147 |b 0 |u fzj |
| 111 | 2 | _ | |a nternational conference on neuromorphic, natural and physical computing |g NNPC |c Hanover |d 2023-10-25 - 2023-10-27 |w Germany |
| 245 | _ | _ | |a Finding new bio-plausible Learning Rules using Deep Reinforcement Learning |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 502 | _ | _ | |c RWTH Aachen |
| 520 | _ | _ | |a Gradient-based learning is still the best bet when training spiking neural networks on supervised tasks. Although backpropagation, the state-of-the-art in modern AI, is not bio-plausible, there exists a wide range of approximations with this property that achieve competitive performance, e.g. e-prop[1,2]. We propose a new framework called AlphaGrad that could find more such learning rules by systematically exploring the search space using Deep Reinforcement Learning and methods from Automatic Differentiation(AD). |
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| 700 | 1 | _ | |a Neftci, Emre |0 P:(DE-Juel1)188273 |b 1 |u fzj |
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