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Samuel Eckmann
Samuel Eckmann
Computational and Biological Learning Lab, University of Cambridge
Verified email at eng.cam.ac.uk
Title
Cited by
Cited by
Year
Active efficient coding explains the development of binocular vision and its failure in amblyopia
S Eckmann, L Klimmasch, BE Shi, J Triesch
Proceedings of the National Academy of Sciences 117 (11), 6156-6162, 2020
252020
Synapse-type-specific competitive Hebbian learning forms functional recurrent networks
S Eckmann, EJ Young, J Gjorgjieva
Proceedings of the National Academy of Sciences 121 (25), e2305326121, 2024
72024
The fisher information as a neural guiding principle for independent component analysis
R Echeveste, S Eckmann, C Gros
Entropy 17 (6), 3838-3856, 2015
72015
A computational model for the joint development of accommodation and vergence control
J Triesch, S Eckmann, B Shi
Journal of Vision 17 (10), 162-162, 2017
42017
A model of the development of anisometropic amblyopia through recruitment of interocular suppression
S Eckmann, L Klimmasch, B Shi, J Triesch
Journal of Vision 18 (10), 942-942, 2018
12018
A Computational Model of the Development and Treatment of Anisometropic Amblyopia
S Eckmann, L Klimmasch, BE Shi, J Triesch
PERCEPTION 48, 49-49, 2019
2019
An Active Efficient Coding Model of the Development of Amblyopia
S Eckmann, L Klimmasch, B Shi, J Triesch
2018
An objective function for Hebbian self-limiting synaptic plasticity rules
C Gros, S Eckmann, R Echeveste
APS March Meeting Abstracts 2016, E41. 001, 2016
2016
Should Hebbian learning be selective for negative excess kurtosis?
C Gros, S Eckmann, R Echeveste
BMC Neuroscience 16 (Suppl 1), P65, 2015
2015
Cubic Learning Rules for Unsupervised Self-Limiting Hebbian Learning in Artificial Neural Networks
S Eckmann
Institute for Theoretical Physics, Goethe University, Frankfurt am Main, 2015
2015
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Articles 1–10