O
Olivier Rochel
Researcher at French Institute for Research in Computer Science and Automation
Publications - 12
Citations - 969
Olivier Rochel is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Spiking neural network & Artificial neural network. The author has an hindex of 6, co-authored 12 publications receiving 895 citations. Previous affiliations of Olivier Rochel include University of Leeds.
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Journal ArticleDOI
Simulation of networks of spiking neurons: A review of tools and strategies
Romain Brette,Michelle Rudolph,Ted Carnevale,Michael L. Hines,David Beeman,James M. Bower,Markus Diesmann,Markus Diesmann,Abigail Morrison,Philip H. Goodman,Frederick C. Harris,Milind Zirpe,Thomas Natschläger,Dejan Pecevski,G. Bard Ermentrout,Mikael Djurfeldt,Anders Lansner,Olivier Rochel,Thierry Viéville,Eilif Muller,Andrew P. Davison,Sami El Boustani,Alain Destexhe +22 more
TL;DR: In this paper, a review of different aspects of the simulation of spiking neural networks is presented, with the aim of identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking networks.
Posted Content
Simulation of networks of spiking neurons: A review of tools and strategies
Romain Brette,Michelle Rudolph,Ted Carnevale,Michael L. Hines,David Beeman,James M. Bower,Markus Diesmann,Abigail Morrison,Philip H. Goodman,Frederick C. Harris,M. Zirpe,Thomas Natschläger,Dejan Pecevski,B. Ermentrout,Mikael Djurfeldt,Anders Lansner,Olivier Rochel,Thierry Viéville,Eilif Muller,Andrew P. Davison,S. El Boustani,Alain Destexhe +21 more
TL;DR: This work provides a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies.
Book ChapterDOI
Contour Detection by Synchronization of Integrate-and-Fire Neurons
TL;DR: A biologically inspired spiking neural network which is able to detect contours in grey level images by synchronization of neurons, made of integrate-and-fire neurons, spaced on a triangular network, whose oriented receptive field is constructed by a wavelet which specifically detects edges.
Posted Content
Introducing numerical bounds to improve event-based neural network simulation
TL;DR: A fast minimal complementary alternative with respect to existing simulation event-based methods, with the possibility to simulate interesting neuron models is implemented and experimented.
From the decoding of cortical activities to the control of a JACO robotic arm: a whole processing chain
TL;DR: In this paper, a complete processing chain for decoding intracranial data recorded in the cortex of a monkey and replicating the associated movements on a JACO robotic arm by Kinova is presented.