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@INPROCEEDINGS{Lohoff:1021872,
author = {Lohoff, Jamie and Neftci, Emre},
title = {{F}inding new bio-plausible {L}earning {R}ules using {D}eep
{R}einforcement {L}earning},
school = {RWTH Aachen},
reportid = {FZJ-2024-01020},
year = {2023},
abstract = {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).},
month = {Oct},
date = {2023-10-25},
organization = {nternational conference on
neuromorphic, natural and physical
computing, Hanover (Germany), 25 Oct
2023 - 27 Oct 2023},
subtyp = {After Call},
cin = {PGI-15},
cid = {I:(DE-Juel1)PGI-15-20210701},
pnm = {5234 - Emerging NC Architectures (POF4-523) / BMBF
16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
der künstlichen Intelligenz für die Elektronik der Zukunft
- NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
Verbundprojekt: Neuro-inspirierte Technologien der
künstlichen Intelligenz für die Elektronik der Zukunft -
NEUROTEC II - (BMBF-16ME0399)},
pid = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
G:(DE-82)BMBF-16ME0399},
typ = {PUB:(DE-HGF)24},
doi = {10.34734/FZJ-2024-01020},
url = {https://juser.fz-juelich.de/record/1021872},
}