Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011)

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Accession:144450
The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.
Reference:
1 . Gómez González JF, Mel BW, Poirazi P (2011) Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time. Front Comput Neurosci 5:44 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
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CA1_Gomez_2011
lib
basic-graphics.hoc *
choose-secs.hoc *
current-balance.hoc
cut-sections.hoc *
deduce-ratio.hoc *
find-gmax.hoc
histographBP_TP02a.hoc
histographBP_TP02b.hoc
histographBP_TP02b_button.hoc
jose.hoc
map-segments-to-3d.hoc *
morphology-lib.hoc
Oblique-lib.hoc *
Oblique-lib2.hoc
salloc.hoc *
spikecount.hoc *
TP-lib.hoc *
tune-epsps.hoc
tune-epspsN128.hoc
tune-epspsSOMA.hoc
vector-distance.hoc
vector-distanceORIGINAL.hoc *
verbose-system.hoc *
                            
//--------------------------------------------- PRUNING --------------------------------------------------




if (Apical_pruning){
		if (ratio_Pruning==1){
			remove_sections(TP_apical_list,L_Pruning)
		}else{
		random_remove_sections(TP_apical_list,ratio_Pruning,L_Pruning_ob,L_Pruning_tu,ob,vector_distance_Apic_TP_b)
		}

}



if (Basal_pruning){
		if (ratio_Pruning==1){
				remove_sections(TP_basal_list,L_Pruning)
		}else{
		random_remove_sections(TP_basal_list,ratio_Pruning,0,L_Pruning_b,0,vector_distance_Basal_TP_b)
		}
}
 


//--------------------------------------------End PRUNING--------------------------------------------------
print "GGGGGGGGGGGGGGGGGGGGGGGGGGGG   AFTER PRUNING  GGGGGGGGGGGGGGGGGGGGGGGGGGGGGG"
//------------------AGAIN------------------ To calculate the morphological parameter --------------------------

n_soma=n_section("soma")
n_dend=n_section("dendrite")
n_apic=n_section("apical_dendrite")

print "Number of sections in\n   soma: ",n_soma," dendrite: ",n_dend," apical dendrite: ",n_apic

dendrite_n_list= n_list(basal_tree_list)
apical_dentrite_n_list=n_list(apical_non_trunk_list)

print "Number of real elements(axon and trunk removed) of:" 
print "   drendrites: ",dendrite_n_list," apical dendrites: ",apical_dentrite_n_list


	
Terminal_Parent(apical_non_trunk_list,apical_trunk_list,"Apical dendrites",display_Apical_Dendrite_TP)
matrix_apical_dendrite_TP = matrix_coord.c()
vector_L_Apic_TP=vector_L.c
TP_apical_list=TP_list 

Terminal_Parent(basal_tree_list,soma_list,"Dedrites",display_Dendrite_TP)
matrix_dendrite_TP = matrix_coord.c()
vector_L_Basal_TP=vector_L.c
TP_basal_list=TP_list

Branch_Child_Parent(apical_non_trunk_list,apical_trunk_list,"Apical dendrites",display_Apical_Dendrite_BP)
matrix_apical_dendrite_BP = matrix_coord.c()
BP_apical_list=BP_list

Branch_Child_Parent(basal_tree_list,soma_list,"Dedrites",display_Dendrite_BP)
matrix_dendrite_BP = matrix_coord.c()
BP_basal_list=BP_list

//---------------AGAIN---------------------END  To calculate the morphologycal parameter --------------------------

//---------------AGAIN------------------- To calculate the radial disance to de center of the soma -----------

distanceTosoma(matrix_apical_dendrite_TP,xcg,ycg,zcg,"Distance apical dendrite TP",display_Radial_Distance_Apical_Dendrite_TP)
vector_distance_Apic_TP_b=vector_distance.c

distanceTosoma(matrix_dendrite_TP,xcg,ycg,zcg,"Distance  basal dendrite TP",display_Radial_Distance_Dendrite_TP)
vector_distance_Basal_TP_b=vector_distance.c

distanceTosoma(matrix_apical_dendrite_BP,xcg,ycg,zcg,"Distance apical dendrite BP",display_Radial_Distance_Apical_Dendrite_BP)
vector_distance_Apic_BP_b=vector_distance.c

distanceTosoma(matrix_dendrite_BP,xcg,ycg,zcg,"Distance basal dendrite BP",display_Radial_Distance_Dendrite_BP)
vector_distance_Basal_BP_b=vector_distance.c

//-----------------------Shell-wise Dendrite Path Length for pruned neuron---------------------------

Sholl_Path_Length(apical_non_trunk_list,R,dR,xcg,ycg,zcg)
PL_Sholl_apical_b=PL_Sholl

Sholl_Path_Length(basal_tree_list,R,dR,xcg,ycg,zcg)
PL_Sholl_basal_b=PL_Sholl

PL_Sholl_apical_b=PL_Sholl_apical_b.reverse

xy2.erase()
xy2.beginline()

for(k=-R/dR;k<0;k+=1){
	xy2.line((k)*dR,PL_Sholl_apical_b.x[k+R/dR])
}
xy2.line(0,0)			//I put this point 
for(k=1;k<(R/dR);k+=1){
	xy2.line((k)*dR,PL_Sholl_basal_b.x[k-1])
}

xy2.flush()


//---------------AGAIN-----------------------------To calculate the Dendritic path lenght    ---------------------


lenghtTosoma(TP_apical_list)
L_TP_apical_b=L_dend.c
lenghtTosoma(BP_apical_list)
L_BP_apical_b=L_dend.c
lenghtTosoma(TP_basal_list)
L_TP_basal_b=L_dend.c
lenghtTosoma(BP_basal_list)
L_BP_basal_b=L_dend.c


xopen_filehoc(root_lib,"histographBP_TP02b_button")		///ESTO HAY QUE CAMBIARLO, CREAR OTRO FICHERO PARA ESTE CASO


//----------AGAIN-----------To calculate the combined Dendritic length for pruned neuron----------------------



DL1_b=(combined_DL(TP_apical_list)+combined_DL(BP_apical_list))  // Total apical dendritic length 
DL2_b=(combined_DL(TP_basal_list)+combined_DL(BP_basal_list))	// Total basal dendritic length 
								// Total apical trunk length is the same
DL_b=DL1_b+DL2_b+DL3
reduced_DL1=(DL1_a-DL1_b)*100/DL1_a
reduced_DL2=(DL2_a-DL2_b)*100/DL2_a
reduced_DL=(DL_a-DL_b)*100/DL_a
print "Combined dendritic lengths"
print "\nApical dendritic length  ", DL1_a, "in pruned neuron: ",DL1_b , " % : ", reduced_DL1
print "Basal dendritic length    ",DL2_a, "in pruned neuron: ",DL2_b , " % : ", reduced_DL2
print "Apical trunk length       ",DL3
print "\nTotal dendritic length   ",DL_a, "in pruned neruon: ",DL_b, " %  :",reduced_DL



//------------------------------------------ Display NEURON-------------------------------------------------


	display.color_list(apical_trunk_list,6)
	display.color_list(axon_sec_list,3)
	display.color_list(soma_list,2)
	display.color_list(basal_tree_list,4)
	display.color_list(apical_non_trunk_list,5)
	display.flush()

//------------------------------------------End Display NEURON----------------------------------------------


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