Parallelizing large networks in NEURON (Lytton et al. 2016)

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Accession:188544
"Large multiscale neuronal network simulations and innovative neurotechnologies are required for development of these models requires development of new simulation technologies. We describe here the current use of the NEURON simulator with MPI (message passing interface) for simulation in the domain of moderately large networks on commonly available High Performance Computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike passing paradigm and post-simulation data storage and data management approaches. We also compare three types of networks, ..."
Reference:
1 . Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schürmann F, Hines ML (2016) Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput 28:2063-90 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron; Abstract Izhikevich neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; NetPyNE;
Model Concept(s): Simplified Models; Methods; Multiscale;
Implementer(s): Dura-Bernal, Salvador [salvadordura at gmail.com]; Lytton, William [bill.lytton at downstate.edu]; Seidenstein, Alexandra [ahs342 at nyu.edu];
"""
init.py

Startup script to run model.

Model was developed using NetPyNE, a python package to facilitate the development, 
parallel simulation and analysis of biological neuronal networks using the NEURON simulator.

www.neurosimlab.org/netpyne

Usage:
    python init.py # Run simulation, optionally plot a raster

MPI usage:
    mpiexec -n 4 nrniv -python -mpi init.py

Contributors: salvadordura@gmail.com
"""

from netpyne import sim
import HHNet, IzhiNet, HybridNet


###############################################################################
# Sequence of commands to run full model
###############################################################################
def runModel():
    sim.initialize(                
        simConfig = HHNet.simConfig, 
        netParams = HHNet.netParams)  
    sim.net.createPops()                  # instantiate network populations
    sim.net.createCells()                 # instantiate network cells based on defined populations
    sim.net.connectCells()                # create connections between cells based on params
    sim.setupRecording()                  # setup variables to record for each cell (spikes, V traces, etc)
    sim.runSim()                          # run parallel Neuron simulation  
    sim.gatherData()                      # gather spiking data and cell info from each node
    sim.saveData()                        # save params, cell info and sim output to file (pickle,mat,txt,etc)
    sim.analysis.plotData()               # plot spike raster

runModel()                              # execute sequence of commands to run full model

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