Samuel Eckmann
Samuel Eckmann
Computational and Biological Learning Lab, University of Cambridge
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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
The fisher information as a neural guiding principle for independent component analysis
R Echeveste, S Eckmann, C Gros
Entropy 17 (6), 3838-3856, 2015
Synapse-type-specific competitive Hebbian learning forms functional recurrent networks
S Eckmann, J Gjorgjieva
bioRxiv, 2022.03. 11.483899, 2022
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
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
A Computational Model of the Development and Treatment of Anisometropic Amblyopia
S Eckmann, L Klimmasch, BE Shi, J Triesch
PERCEPTION 48, 49-49, 2019
An Active Efficient Coding Model of the Development of Amblyopia
S Eckmann, L Klimmasch, B Shi, J Triesch
An objective function for Hebbian self-limiting synaptic plasticity rules
C Gros, S Eckmann, R Echeveste
APS March Meeting Abstracts 2016, E41. 001, 2016
Should Hebbian learning be selective for negative excess kurtosis?
C Gros, S Eckmann, R Echeveste
BMC Neuroscience 16 (Suppl 1), P65, 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
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