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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; 
import sys
import matplotlib.pyplot as plt

def plot_bio_and_ele_calcium(spine, controls):
	timeSeries = "timeSeries_%s" %spine
	ecell_time_equilibrium = 300 #s
	t_bio = controls.manager.groups[timeSeries]
    	t_bio_ms = (t_bio.read() - ecell_time_equilibrium) * 1e3
	
    	ca_conc = controls.manager.get_vector(spine, 'ca_conc', group=timeSeries)
	label = "bio in %s" %spine
    	plt.plot(t_bio_ms, ca_conc.read()*1e6, label=label)
    	plt.title("Bio")
	plt.xlabel('Time [ms]')
	plt.ylabel('Concentration [um]')
        
	neuron_time_equilibrium = 100 #ms
        spine_head = "%s_head" %spine
	cai = controls.manager.get_vector(spine_head, 'cai')
    	cali = controls.manager.get_vector(spine_head, 'cali')
	t = controls.manager.groups['t']
	plt.figure()
	label = "electrical in %s" %spine
	plt.plot((t.read()-neuron_time_equilibrium), ((cali.read()+cai.read())*1e3), label=label)
	plt.xlabel('Time [ms]')
	plt.ylabel('Concentration [um]')
    	plt.title("Ele")
	xlim = plt.xlim(0,) #starting from zero
	plt.figure(1)
	plt.xlim(xlim) # Getting bio graph on the electric xlim.

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