J
Jacques Kaiser
Researcher at Center for Information Technology
Publications - 33
Citations - 816
Jacques Kaiser is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Spiking neural network & Neurorobotics. The author has an hindex of 12, co-authored 33 publications receiving 514 citations. Previous affiliations of Jacques Kaiser include French Institute for Research in Computer Science and Automation.
Papers
More filters
Journal ArticleDOI
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
TL;DR: Recently, Deep Continuous Local Learning (DECOLLE) as mentioned in this paper has been proposed to learn deep spatio-temporal representations from spikes relying solely on local information using synthetic gradients.
Journal ArticleDOI
Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform.
Egidio Falotico,Lorenzo Vannucci,Alessandro Ambrosano,Ugo Albanese,Stefan Ulbrich,Juan Camilo Vasquez Tieck,Georg Hinkel,Jacques Kaiser,Igor Peric,Oliver Denninger,Nino Cauli,Murat Kirtay,Arne Roennau,Gudrun Klinker,Axel von Arnim,Luc Guyot,Daniel Peppicelli,Pablo Martínez-Cañada,Eduardo Ros,Patrick Maier,Sandro Weber,Manuel Huber,David A. Plecher,Florian Röhrbein,Stefan Deser,Alina Roitberg,Patrick van der Smagt,Rudiger Dillman,Paul Levi,Cecilia Laschi,Alois Knoll,Marc-Oliver Gewaltig +31 more
TL;DR: This work presents the architecture of the first release of the Neurorobotics Platform, a new web-based environment offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation.
Journal ArticleDOI
Simultaneous State Initialization and Gyroscope Bias Calibration in Visual Inertial Aided Navigation
TL;DR: It is shown that the gyroscope bias, not accounted for in [1], significantly affects the performance of the closed-form solution and a new method is introduced to automatically estimate this bias and is robust to it.
Posted Content
Synaptic Plasticity Dynamics for Deep Continuous Local Learning.
TL;DR: Recently, Deep Continuous Local Learning (DECOLLE) as discussed by the authors has been proposed to learn deep spatio-temporal representations from spikes relying solely on local information using synthetic gradients.
Proceedings ArticleDOI
Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks
Jacques Kaiser,J. Camilo Vasquez Tieck,Christian Hubschneider,Peter Wolf,Michael Weber,Michael Hoff,Alexander Friedrich,Konrad Wojtasik,Arne Roennau,Ralf Kohlhaas,Rüdiger Dillmann,J. Marius Zollner +11 more
TL;DR: A spiking neural network which controls a vehicle end-to-end for lane following behavior is demonstrated and could be used to design more complex networks and use the evaluation metrics for learning.