Models of visual topographic map alignment in the Superior Colliculus (Tikidji-Hamburyan et al 2016)

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Accession:195658
We develop two novel computational models of visual map alignment in the SC that incorporate distinct activity-dependent components. First, a Correlational Model assumes that V1 inputs achieve alignment with established retinal inputs through simple correlative firing mechanisms. A second Integrational Model assumes that V1 inputs contribute to the firing of SC neurons during alignment. Both models accurately replicate in vivo findings in wild type, transgenic and combination mutant mouse models, suggesting either activity-dependent mechanism is plausible.
Reference:
1 . Tikidji-Hamburyan RA, El-Ghazawi TA, Triplett JW (2016) Novel Models of Visual Topographic Map Alignment in the Superior Colliculus. PLoS Comput Biol 12:e1005315 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Superior colliculus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Cython; Python;
Model Concept(s): Connectivity matrix; Development; Vision;
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] ;
"""
/***********************************************************************************************************\

This Python script is parameters parser for EnergyChaser.pyx library associated with paper:                                                        
 Ruben Tikidji-Hamburyan , Tarek El-Ghazawi , Jason Triplett
    Novel Models of Visual Topographic Map Alignment into Superior Colliculus

 Copyright: Ruben Tikidji-Hamburyan <rath@gwu.edu> Apr.2016 - Sep.2016

\************************************************************************************************************/    
"""
import os,sys,csv
from numpy import *
from numpy import random as rnd
#from multiprocs import multiprocs as mps
import subprocess as sbp
try:
	import cPickle as pkl
except:
	import pickle as pkl

import time
import pyximport; pyximport.install()
from EnergyChaser import chaser
			
########## DEFAULTs HERE ##########
#Size
xsize,  ysize	= 100, 100

#Chemistri
Aaf, Baf		= 60., 90.
#Activity
Bca, Gca		= 11., 20.
Rca, Vca, Sca	= 5., 3.,1.
A2Pca			= 0.625
#Competition
Apr, Bpr, Dpr	= 5., 1., 1.

E12				= 1e5
Nstep			= 150 #number steps per neuron
ParentDir		= ""

#Flags
Knocked			= False

Report			= False
TotalEnergy		= False
Init			= True
StartRec		= False
StopRec			= True
Indicator		= False
Graphs			= False
Log				= True
RunDB			= True
Model			= 3 #"ScaledCor" #"Correlation" or "V1Int"
Norm			= True
ModelID			= time.strftime("%Y%m%d%H%M%S")+"%03d"%rnd.randint(1000)
##################################
	
if __name__ == "__main__":
	for arg in sys.argv[1:]:
		if arg[0] != "/": continue
		if not "="in arg[1:] : continue
		print "Applay: ",arg[1:],
		try:
			exec arg[1:]
		except:
			sys.stderr.write("ERROR in parameter {}\n\n".format(arg))
		print " Done"

if type(Model) is str:
	if   Model == "ScaledCor"   : Model = 2
	elif Model == "Correlation" : Model = 1
	elif Model == "V1Int"       : Model = 3
	elif Model == "V1Int-D"     : Model = 4
	else                        : Model = 3
if Model != 1 and Model != 2 and Model != 3 and Model != 4 : Model = 3

p = chaser(xsize, ysize, Nstep,
			E12 = E12,
			Aaf = Aaf, Baf = Baf, 
			Bca = Bca, Gca = Gca, Rca=Rca, Vca = Vca, Sca=Sca, A2Pca = A2Pca,
			Apr = Apr, Bpr = Bpr, Dpr = Dpr,
			TotalEnergy	= TotalEnergy, Init      = Init,
			StartRec    = StartRec   , StopRec   = StopRec,
			Knocked     = Knocked    , Indicator = Indicator,
			Log         = Log        , Reporting = Report,
			Graphs      = Graphs     , RunDB     = RunDB,
			Model       = Model      , Norm      = Norm,
			ParentDir = ParentDir    , timestemp = ModelID)