Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019)

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1 . Tikidji-Hamburyan RA, Leonik CA, Canavier CC (2019) Phase response theory explains cluster formation in sparsely but strongly connected inhibitory neural networks and effects of jitter due to sparse connectivity. J Neurophysiol 121:1125-1142 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract single compartment conductance based cell;
Gap Junctions:
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at] ;
import simmethods, os

def getconfig(name,default):
	@function getconfig reads configuration parameters and returns a value
	@param  name    - name of parameter
	@param  default - default value
	Function reads first /etc/simtools.conf, then .simtoolsrc in home user 
	directory and then .simtoolsrc in current directory therefore all values
	in local .simtoolsrc have highest priority, while in /etc/simtools.conf 
	have lowest priority
	simdbvars = simmethods.simmethods(presets=[name+"={}".format(default)], target="simdbvars", localcontext=locals())
	#First global
	infile = "/etc/simtools.conf"
	if os.access(infile,os.R_OK):simdbvars.readfile(infile)
	#Second global for user
	infile = os.environ['HOME']+"/.simtoolsrc"
	if os.access(infile,os.R_OK):simdbvars.readfile(infile)
	#Third local in folder
	infile = ".simtoolsrc"
	if os.access(infile,os.R_OK):simdbvars.readfile(infile)
	return simdbvars[name]		

if __name__ == "__main__":
	print "X"
	print getconfig("/deep",12)
	print getconfig("/editor",'None')