A network model of the vertebrate retina (Publio et al. 2009)

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Accession:124063
In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics.
Reference:
1 . Publio R, Oliveira RF, Roque AC (2009) A computational study on the role of gap junctions and rod Ih conductance in the enhancement of the dynamic range of the retina. PLoS One 4:e6970 [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): Retina ganglion GLU cell; Retina photoreceptor cone GLU cell; Retina bipolar GLU cell;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Sensory processing;
Implementer(s): Publio, Rodrigo [publio at oist.jp];
Search NeuronDB for information about:  Retina ganglion GLU cell; Retina photoreceptor cone GLU cell; Retina bipolar GLU cell; Glutamate;
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PublioEtAl2009
README.html
A2hh_k.mod
A2hh_na.mod
Bip_Ca.mod
Bip_Cad.mod
Bip_ih.mod
Bip_Ka.mod
Bip_Kv.mod
Cone_CPR.mod
Cone_ih.mod
Cone_Kv.mod
Ganglion_hh.mod *
gap.mod
IinjLT.mod
IinjLT_cone.mod
IinjLTDim.mod *
Rod_Ca.mod
Rod_Cad.mod
Rod_Clca.mod
Rod_ih.mod
Rod_Kca.mod
Rod_Kv.mod
Rod_Kx.mod
Rod_leak.mod
syn_bip_gan.mod
syn_rod_bip.mod
A2.tem
Bip.tem
Cone.tem
createcells.hoc
Ganglion.tem
gap.hoc *
init.hoc
mosinit.hoc *
netconnection.hoc
parameters.hoc
Rod.tem
screenshot1.jpg
screenshot2.jpg
session.ses
                            
// Parameters for Init.hoc

// Simulation Parameters

celsius = 23			// from Chen et al bipolar cell
v_init = -41.5
ENABLE_GRAPHICAL_INTERFACE = 1
MAXNUMBERCONNECTIONS = 50000
seed        = 0			// seed for random number generator
tstop	    = 6000		// ms  total simulation time
stim_init = 100
step_dt 	    = 0.1		// ms
runStopAt = tstop
steps_per_ms = 1/dt
number_steps = steps_per_ms*tstop
count=0
amp=50
gihbar=2.5
PROB=0.5


// Network parameters

//First gap layer

nrodx      =  50		// 1st dimension of rod cell array
nrody      =  30		// 2nd dimension of rod cell array
nconex       = 4		// 1st dimension of cone cell array
nconey       = 4		// 2nd dimension of cone cell array
rodtotal   = nrodx*nrody	// total number of Rods
conetotal    = nconex*nconey	// total number of Cones

//Bipolar layer

nrodbipx       = 10			// 1st dimension of bipolar cell array
nrodbipy       = 10			// 2nd dimension of bipolar cell array
nconebipx       = 2			// 1st dimension of bipolar cell array
nconebipy       = 2			// 2nd dimension of bipolar cell array
biptotal    = nrodbipx*nrodbipy	+ nconebipx*nconebipy

//A2 layer

na2x      =  3			// 1st dimension of A2 cell array
na2y      =  3			// 2nd dimension of A2 cell array
A2total   = na2x*na2y

// Output

nganx      =  1		// 1st dimension of Ganglion cell array
ngany      =  1		// 2nd dimension of Ganglion cell array
gantotal   = nganx*ngany


// Divergence

rodtobip    = 2
conetobip    = 1
conetorod = 4	
rodbiptoa2 = 3
a2toconebip = 2
conebiptogan = 1









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