Abigail Morrison
Abigail Morrison
Jülich Research Center
Verified email at fz-juelich.de - Homepage
Cited by
Cited by
Simulation of networks of spiking neurons: a review of tools and strategies
R Brette, M Rudolph, T Carnevale, M Hines, D Beeman, JM Bower, ...
Journal of computational neuroscience 23 (3), 349-398, 2007
Phenomenological models of synaptic plasticity based on spike timing
A Morrison, M Diesmann, W Gerstner
Biological cybernetics 98 (6), 459-478, 2008
Spike-timing-dependent plasticity in balanced random networks
A Morrison, A Aertsen, M Diesmann
Neural computation 19 (6), 1437-1467, 2007
Advancing the boundaries of high-connectivity network simulation with distributed computing
A Morrison, C Mehring, T Geisel, AD Aertsen, M Diesmann
Neural computation 17 (8), 1776-1801, 2005
Exact subthreshold integration with continuous spike times in discrete-time neural network simulations
A Morrison, S Straube, HE Plesser, M Diesmann
Neural computation 19 (1), 47-79, 2007
Efficient parallel simulation of large-scale neuronal networks on clusters of multiprocessor computers
HE Plesser, JM Eppler, A Morrison, M Diesmann, MO Gewaltig
European Conference on Parallel Processing, 672-681, 2007
Spiking network simulation code for petascale computers
S Kunkel, M Schmidt, JM Eppler, HE Plesser, G Masumoto, J Igarashi, ...
Frontiers in neuroinformatics 8, 78, 2014
A spiking neural network model of an actor-critic learning agent
W Potjans, A Morrison, M Diesmann
Neural computation 21 (2), 301-339, 2009
Supercomputers ready for use as discovery machines for neuroscience
M Helias, S Kunkel, G Masumoto, J Igarashi, JM Eppler, S Ishii, T Fukai, ...
Frontiers in neuroinformatics 6, 26, 2012
Meeting the memory challenges of brain-scale network simulation
S Kunkel, TC Potjans, JM Eppler, HEE Plesser, A Morrison, M Diesmann
Frontiers in neuroinformatics 5, 35, 2012
A general and efficient method for incorporating precise spike times in globally time-driven simulations
A Hanuschkin, S Kunkel, M Helias, A Morrison, M Diesmann
Frontiers in neuroinformatics 4, 113, 2010
Limits to the development of feed-forward structures in large recurrent neuronal networks
S Kunkel, M Diesmann, A Morrison
Frontiers in computational neuroscience 4, 160, 2011
An imperfect dopaminergic error signal can drive temporal-difference learning
W Potjans, M Diesmann, A Morrison
PLoS computational biology 7 (5), e1001133, 2011
Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
YV Zaytsev, A Morrison, M Deger
Journal of computational neuroscience 39 (1), 77-103, 2015
Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity
W Potjans, A Morrison, M Diesmann
Frontiers in computational neuroscience 4, 141, 2010
Automatic generation of connectivity for large-scale neuronal network models through structural plasticity
S Diaz-Pier, M Naveau, M Butz-Ostendorf, A Morrison
Frontiers in neuroanatomy 10, 57, 2016
Nest 2.12. 0
S Kunkel, R Deepu, HE Plesser, B Golosio, ME Lepperød, JM Eppler, ...
Jülich Supercomputing Center, 2017
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
A Hanuschkin, M Diesmann, A Morrison
Journal of computational neuroscience 31 (3), 509-532, 2011
NineML: the network interchange for ne uroscience modeling language
I Raikov, R Cannon, R Clewley, H Cornelis, A Davison, E De Schutter, ...
BMC neuroscience 12 (1), 1-2, 2011
NEST: the Neural Simulation Tool.
HE Plesser, M Diesmann, MO Gewaltig, A Morrison
Encyclopedia of Computational Neuroscience, 1849-1852, 2014
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