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Abigail Morrison
Researcher at Ruhr University Bochum
Publications - 94
Citations - 4006
Abigail Morrison is an academic researcher from Ruhr University Bochum. The author has contributed to research in topics: Computer science & Spiking neural network. The author has an hindex of 24, co-authored 75 publications receiving 3608 citations. Previous affiliations of Abigail Morrison include Allen Institute for Brain Science & University of Freiburg.
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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.
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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.
Journal ArticleDOI
Spike-Timing-Dependent Plasticity in Balanced Random Networks
TL;DR: A novel STDP update rule is proposed, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation, and it is shown that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime.
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Advancing the Boundaries of High-Connectivity Network Simulation with Distributed Computing
TL;DR: This work presents a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron.