M
Markus Diesmann
Researcher at RWTH Aachen University
Publications - 266
Citations - 13550
Markus Diesmann is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Population & Spiking neural network. The author has an hindex of 53, co-authored 260 publications receiving 11970 citations. Previous affiliations of Markus Diesmann include Hebrew University of Jerusalem & RIKEN Brain Science Institute.
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Stable propagation of synchronous spiking in cortical neural networks
TL;DR: The results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.
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Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function
TL;DR: Findings indicate that internally generated synchronization of individual spike discharges may subserve the cortical organization of cognitive motor processes.
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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.
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Phenomenological models of synaptic plasticity based on spike timing
TL;DR: This document reviews phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP), and focuses on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables.