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Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)
 
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Simulation Platform
Accession:
53437
A short introduction to how "integrate and fire" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time.
Reference:
1 .
Hines ML, Carnevale NT (2004) Discrete event simulation in the NEURON environment.
Neurocomputing
58-60
:1117-1122
Model Information
(Click on a link to find other models with that property)
Model Type:
Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment:
NEURON;
Model Concept(s):
Tutorial/Teaching;
Methods;
Implementer(s):
Hines, Michael [Michael.Hines at Yale.edu];
Download the displayed file
/
hines2004
README
cells.ses
init.hoc
mosinit.hoc
*
Other models using mosinit.hoc:
DCN fusiform cell (Ceballos et al. 2016)
Spontaneous firing caused by stochastic channel gating (Chow, White 1996)
Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)
net.ses
rig.ses
load_file("init.hoc")
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