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

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Accession:239177

Reference:
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]
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;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] ;
"""
Template for single spike source

Coded: Ruben A. Tikidji-Hamburyan

"""


import os,sys,csv
from numpy import *
from numpy import random as rnd
import scipy as sp
import matplotlib
matplotlib.rcParams["savefig.directory"] = ""
from matplotlib.pyplot import *
try:
	import cPickle as pkl
except:
	import pickle as pkl
from neuron import h

class Sg:
	def __init__(self, init=[0,0.]):
		self.soma = h.Section() #dummy compartment
		self.output = h.VecStim()
		self.id = init[0]
		self.tS = init[1]
		#Spike Recorder
		self.spks	= h.Vector()
		self.recorder = h.NetCon(self.output,None,sec=self.soma)
		self.recorder.threshold = 0.
		self.recorder.record(self.spks)
	def activate(self,sequence):			
		self.src = h.Vector(sequence.shape[0])
		self.src.from_python(sequence)
		self.output.play(self.src)

if __name__ == "__main__":
	g = Sg()
	g.activate(lambda i,t: t+random.random()*100, 0, 500)
	print array(g.src)


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