Neural mechanisms underlying sensitivity to reverse-phi motion in the fly.

Autor: Aljoscha Leonhardt, Matthias Meier, Etienne Serbe, Hubert Eichner, Alexander Borst

Publikováno v: PLoS ONE, Vol 12, Iss 12, p e0189019 (2017)

Optical illusions provide powerful tools for mapping the algorithms and circuits that underlie visual processing, revealing structure through atypical function. Of particular note in the study of motion detection has been the reverse-phi illusion. Wh

Externí odkaz: https://doaj.org/article/ee347c9218964deb8c8f179dc969285f

Coding efficiency of fly motion processing is set by firing rate, not firing precision.

Autor: Deusdedit Lineu Spavieri, Hubert Eichner, Alexander Borst

Publikováno v: PLoS Computational Biology, Vol 6, Iss 7, p e1000860 (2010)

To comprehend the principles underlying sensory information processing, it is important to understand how the nervous system deals with various sources of perturbation. Here, we analyze how the representation of motion information in the fly's nervou

Externí odkaz: https://doaj.org/article/3dd117b317c9401a95c228e953339b97

Advances and Open Problems in Federated Learning

Autor: Lie He, Sebastian U. Stich, Mariana Raykova, Phillip B. Gibbons, Mehryar Mohri, David Evans, Badih Ghazi, Felix X. Yu, Sen Zhao, Jianyu Wang, Zheng Xu, Weikang Song, Prateek Mittal, Ramesh Raskar, Zachary Garrett, Farinaz Koushanfar, H. Brendan McMahan, Ayfer Ozgur, Mikhail Khodak, Rafael G. L. D'Oliveira, Jakub Konecní, Aurélien Bellet, Arjun Nitin Bhagoji, Hubert Eichner, Han Yu, Adrià Gascón, Ananda Theertha Suresh, Sanmi Koyejo, Praneeth Vepakomma, Josh Gardner, Chaoyang He, Florian Tramèr, Tancrède Lepoint, Salim El Rouayheb, Peter Kairouz, Li Xiong, Kallista Bonawitz, Rasmus Pagh, Tara Javidi, Mehdi Bennis, Dawn Song, Martin Jaggi, Zhouyuan Huo, Hang Qi, Gauri Joshi, Qiang Yang, Richard Nock, Yang Liu, Brendan Avent, Justin Hsu, Rachel Cummings, Graham Cormode, Marco Gruteser, Aleksandra Korolova, Ziteng Sun, Zaid Harchaoui, Ben Hutchinson, Zachary Charles, Daniel Ramage

Publikováno v: Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b1ccc10027ba1ce68ce0210510e8bdc
https://inria.hal.science/hal-02406503v2/document

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Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors

Autor: Felix Schürmann, Michael L. Hines, Hubert Eichner

Publikováno v: Journal of Computational Neuroscience

Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by eac

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5685a53701a74792af3dae68cf07f6ef
http://europepmc.org/articles/PMC2633940