Analytical modelling of temperature effects on an AMPA-type synapse (Kufel & Wojcik 2018)

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Accession:239072
This code was used in the construction of the model developed in the paper. It is a modified version of the simulation developed by Postlethwaite et al. 2007 - for details of modifications refer to the main body of Kufel & Wojcik (2018).
Reference:
1 . Kufel DS, Wojcik GM (2018) Analytical modelling of temperature effects on an AMPA-type synapse. J Comput Neurosci 44:379-391 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: MCell; Python;
Model Concept(s): Methods; Temperature;
Implementer(s): Kufel, Dominik [dominic.kufel at gmail.com];
Search NeuronDB for information about:  AMPA; Glutamate;
#Author: D.Kufel
#Date: 05/01/2018

'''
The following code calculates the skewness and variation of the AMPAR conductances
distribution (for different runs of simulations).
Used in the data analysis of the Monte Carlo simulation.
'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import variation,skew

statelist=["O1","O2","O3","O4"]

listnewO1=[]
listnewO2=[]
listnewO3=[]
listnewO4=[]

for state in statelist:
	
	for vesicle in range(1,10):
		for run_number in range(1,41):
			nStrv=str(vesicle).rjust(3, '0')  
			nStrr=str(run_number).rjust(3, '0') 
			name="AMPAR_"+state+"_"+nStrv+"_"+nStrr+".dat"
			x,y=np.loadtxt(open(name), delimiter=" ", unpack=True)
			print(name, np.max(y))
			if state=="O1":
				listnewO1.append(np.max(y))
			elif state=="O2":
				listnewO2.append(np.max(y))
			elif state=="O3":
				listnewO3.append(np.max(y))
			else:
				listnewO4.append(np.max(y))



arrayO1=np.asarray(listnewO1)
arrayO2=np.asarray(listnewO2)
arrayO3=np.asarray(listnewO3)
arrayO4=np.asarray(listnewO4)


merged=arrayO1*0.1+arrayO2*0.4+arrayO3*0.7+arrayO4
print(merged)
print("CV=",round(variation(merged),2),"Skewness=",round(skew(merged),2))

plt.figure()

plt.hist(merged,bins=25, normed=1,facecolor='black',histtype='bar', ec='white')

plt.xlabel('amplitude (open channels scaled)')
plt.ylabel('Number (norm.)')
plt.show()

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