Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)

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Accession:150284
"… We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. …"
Reference:
1 . Mattioni M, Le Novère N (2013) Integration of biochemical and electrical signaling-multiscale model of the medium spiny neuron of the striatum. PLoS One 8:e66811 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Channel(s): I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q;
Gap Junctions:
Receptor(s):
Gene(s): Kv4.2 KCND2; Kv1.2 KCNA2; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Signaling pathways; Calcium dynamics; Multiscale;
Implementer(s): Mattioni, Michele [mattioni at ebi.ac.uk];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; I Na,p; I Na,t; I T low threshold; I A; I K,Ca; I CAN; I Calcium; I A, slow; I Krp; I R; I Q; 
from neuronvisio.controls import Controls
controls = Controls()
from neuron import h

from neuronControl.nrnManager import NeuronManager

 
import time


bio_filename = "biochemical_circuits/biomd183_loop.eml"
big_spine = True
dt_neuron = 0.025


nrnManager =NeuronManager(bio_filename,
                          big_spine, 
                          dt_neuron, 
                          mod_path="mod", 
                          hoc_path="hoc", 
                          spines_dist='onebranch')

#start= time.time()
#                  
#controls.manager.add_all_vecRef('v') # Adding vector for the variable v
#
#print "Starting time: %s" % start            
#h.load_file('parcom.hoc')
#p = h.ParallelComputeTool()
#p.nthread(4)
#p.multisplit(True)
#
#h.finitialize()
#while h.t < h.tstop:
#    h.fadvance()
#
#end= time.time()
#
#run_time = end-start
#
#print "Ending time: %s. Total time with no parallel: %s" %(end, run_time)
#print "Tstop: %s" %(h.tstop)
#
## file where to save the results
#filename = 'storage.h5'
## Saving the vectors
#controls.manager.save_to_hdf(filename)

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