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 | 32 | 2020 |
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 | 13 | 2024 |
The fisher information as a neural guiding principle for independent component analysis R Echeveste, S Eckmann, C Gros Entropy 17 (6), 3838-3856, 2015 | 7 | 2015 |
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 | 4 | 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 | 1 | 2018 |
Plasticity of Inhibition in Recurrent Circuits S Eckmann Johann Wolfgang Goethe-Universität Frankfurt am Main, 2022 | | 2022 |
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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