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Simon J. Thorpe
Researcher at Centre national de la recherche scientifique
Publications - 171
Citations - 19620
Simon J. Thorpe is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Visual processing & Artificial neural network. The author has an hindex of 58, co-authored 168 publications receiving 18076 citations. Previous affiliations of Simon J. Thorpe include University of Paris & University of Oxford.
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Journal ArticleDOI
Networks of integrate-and-fire neuron using rank order coding A: How to implement spike time dependent Hebbian plasticity
TL;DR: This article presents a generative model for spike time dependent plasticity based on a simplified model of the synaptic kinetic, and extends this model to a simplified models of integrate-and-fire neurons network using rank order coding.
Journal ArticleDOI
NAVIG: Guidance system for the visually impaired using virtual augmented reality
Brian F. G. Katz,Florian Dramas,Gaëtan Parseihian,Olivier Gutierrez,Slim Kammoun,Adrien Brilhault,Lucie Brunet,Mathieu Gallay,Bernard Oriola,Malika Auvray,Philippe Truillet,Michel Denis,Simon J. Thorpe,Christophe Jouffrais +13 more
TL;DR: The overall project design and architecture of the NAVIG system is presented and details of the new type of detection and localization device are presented in relation to guidance directives developed through participative design with potential users and educators for the visually impaired.
Journal ArticleDOI
Rapid categorization of foveal and extrafoveal natural images: Associated ERPs and effects of lateralization
TL;DR: Present results show that this activity originates within extrastriate visual cortices and probably reflects perceptual stimuli differences processed within areas involved in object recognition, and that Latencies, slopes, and peak amplitudes of this differential activity were invariant to stimulus position and attentional load.
Posted Content
Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition
TL;DR: A deep convolutional spiking neural network (DCSNN) and a latency-coding scheme is used that is biologically plausible, hardware friendly, and energy-efficient, and it is demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats.
Proceedings ArticleDOI
Designing an assistive device for the blind based on object localization and augmented auditory reality
TL;DR: This study has tested the possibility of using a sound rendering system to indicate a particular spatial location, and proposes to couple this with a biologically inspired image processing system that can locate visual patterns that correspond to particular objects and places.