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Person

Daniel Brunner (1981-)

Physiker

Name	Daniel Brunner
Time	1981- (Date of birth and death)
Place	Frankreich
Gender	männlich
Academic degree	Dr.
Profession	Physiker (characteristic profession)
Related	Institut FEMTO-ST (Département d'optique / Senior Researcher, CNRS) Instituts Universitaris de Recerca (Postdoctoral researcher)
Source	Internet: https://members.femto-st.fr/daniel-brunner/
Record type	Person
GND-Identifier	120280926X
Record ID	120280926X
Involved in show all Training of physical neural networks Computational memory capacity predicts aging and cognitive decline Annealing-inspired training of an optical neural network with ternary weights Guang zi chu bei chi ji suan : guang xue xun huan shen jing wang luo = Photonic reservoir computing Neural network learning with photonics and for photonic circuit design Combining one and two photon polymerization for accelerated high performance (3 + 1) D photonic integration Machine learning and applications in ultrafast photonics Photonic reservoir computing : optical recurrent neural networks Photonic Reservoir Computing : Optical Recurrent Neural Networks Photonic Reservoir Computing : Optical Recurrent Neural Networks Dynamical complexity and computation in recurrent neural networks beyond their fixed point

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Daniel Brunner (1981-)

Training of physical neural networks

Computational memory capacity predicts aging and cognitive decline

Annealing-inspired training of an optical neural network with ternary weights

Guang zi chu bei chi ji suan : guang xue xun huan shen jing wang luo = Photonic reservoir computing

Neural network learning with photonics and for photonic circuit design

Combining one and two photon polymerization for accelerated high performance (3 + 1) D photonic integration

Machine learning and applications in ultrafast photonics

Photonic reservoir computing : optical recurrent neural networks

Photonic Reservoir Computing : Optical Recurrent Neural Networks

Photonic Reservoir Computing : Optical Recurrent Neural Networks

Dynamical complexity and computation in recurrent neural networks beyond their fixed point