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The recent excitement about neural networks
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2021arXiv210401474B
Thalamocortical contribution to solving credit assignment in neural systems
Brabeeba Wang, Mien; Halassa, Michael M.
2021arXiv210104137B
The backpropagation-based recollection hypothesis: Backpropagated action potentials mediate recall, imagination, language understanding and naming
Ben Houidi, Zied
2020arXiv201011765N
Identifying Learning Rules From Neural Network Observables
Nayebi, Aran; Srivastava, Sanjana;Ganguli, Surya and 1 more
2020NYASA1464..222K
Beyond the feedforward sweep: feedback computations in the visual cortex
Kreiman, Gabriel; Serre, Thomas
2020Senso..20.1459C
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Czimmermann, Tamás; Ciuti, Gastone;Milazzo, Mario and 4 more
2020arXiv200301513K
Two Routes to Scalable Credit Assignment without Weight Symmetry
Kunin, Daniel; Nayebi, Aran;Sagastuy-Brena, Javier and 3 more
2019arXiv191001689G
Spike-based causal inference for weight alignment
Guerguiev, Jordan; Kording, Konrad P.;Richards, Blake A.
2019PLoSO..1418802H
NDRA: A single route model of response times in the reading aloud task based on discriminative learning
Hendrix, Peter; Ramscar, Michael;Baayen, Harald
2019arXiv190713223C
Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation
Comsa, Iulia M.; Potempa, Krzysztof;Versari, Luca and 3 more
2019arXiv190504101I
Biologically plausible deep learning -- but how far can we go with shallow networks?
Illing, Bernd; Gerstner, Wulfram; Brea, Johanni
2019PhRvE..99a0302M
Pattern recognition with neuronal avalanche dynamics
Michiels van Kessenich, L.; Berger, D.;de Arcangelis, L. and 1 more
2018arXiv181103567X
Biologically-plausible learning algorithms can scale to large datasets
Xiao, Will; Chen, Honglin; Liao, Qianliand 1 more
2018NatSR...810651L
Spiking neurons with short-term synaptic plasticity form superior generative networks
Leng, Luziwei; Martel, Roman;Breitwieser, Oliver and 5 more
2017NatSR...711016B
Spatial features of synaptic adaptation affecting learning performance
Berger, Damian L.; de Arcangelis, Lucilla;Herrmann, Hans J.
2017arXiv170511146Z
SuperSpike: Supervised learning in multi-layer spiking neural networks
Zenke, Friedemann; Ganguli, Surya
2016NatCo...713276L
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, Timothy P.; Cownden, Daniel;Tweed, Douglas B. and 1 more
2015PLSCB..11E4489B
Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks
Brosch, Tobias; Neumann, Heiko;Roelfsema, Pieter R.
2015PaReL..64....3P
Pattern recognition between science and engineering: A red herring?
Pelillo, Marcello; Scantamburlo, Teresa;Schiaffonati, Viola
2011AtmRe.102...99S
Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach
Shukla, Ravi P.; Tripathi, Krishna C.;Pandey, Avinash C. and 1 more
2011PLoSO...621575W
Bayesian Cue Integration as a Developmental Outcome of Reward Mediated Learning
Weisswange, Thomas H.;Rothkopf, Constantin A.; Rodemann, Tobiasand 1 more
2007ITEIS.127.1632T
Analysis of Spatio-Temporal Neural Activities by Artificial Neural Network
Takahashi, Hirokazu; Uchihara, Masanobu;Funamizu, Akihiro and 2 more
2006IJMPC..17.1501E
a Heterosynaptic Learning Rule for Neural Networks
Emmert-Streib, Frank
2003cond.mat..7666E
A novel stochastic Hebb-like learning rule for neural networks
Emmert-Streib, Frank
2001JCli...14.2528H
Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using a Neural Network Approach.
Hsieh, William W.
2000PhRvL..84.3013K
Beyond Hebb: Exclusive-OR and Biological Learning
Klemm, Konstantin; Bornholdt, Stefan;Schuster, Heinz Georg
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