L5 PFC pyramidal neurons (Papoutsi et al. 2017)

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Accession:230811
" ... Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types which were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. ..."
Reference:
1 . Papoutsi A, Kastellakis G, Poirazi P (2017) Basal tree complexity shapes functional pathways in the prefrontal cortex. J Neurophysiol 118:1970-1983 [PubMed]
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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: Prefrontal cortex (PFC);
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I A; I h; I L high threshold; I T low threshold; I N; I R; I K,Ca; I_AHP; I_Ks; I Na,p; I Na,t; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA; GabaA; GabaB;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models;
Implementer(s): Papoutsi, Athanasia [athpapoutsi at gmail.com];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I h; I K,Ca; I_Ks; I R; I_AHP; Gaba; Glutamate; 
//Papoutsi,Kastellakis,Poirazi, J. Neurophysiology 2017
//Simulations reproduce Figure 7 (PFC microcircuits)
//Adjusted for single-tasking

//Set simulation setup
load_proc("nrnmainmenu")
cvode_active(0)
tstop=4000
dt=0.1
steps_per_ms=10
setdt()

//Biophysical properties of pyramidal neurons and interneuron 
xopen ("L5PFC_biophysics.hoc") 
xopen("incell.hoc")
//To be used for the microcircuit setup and stimulation
xopen("microcircuit.hoc")
xopen("time_stim.hoc")
xopen("background_noise.hoc")	

//Graphs
xopen("basic-graphics.hoc")
for j=0, (nPcells-1) {addgraph_2("Pcells[j].soma.v(0.5)", 0,tstop, -70, -30)}
addgraph("INcells[0].soma.v",0 ,-70, -50, tstop)

//Synaptic Weights
nmdaweight = 	0.2  			//Pyramidal to pyramidal NMDA conductance, for 1.1 iNMDA-to-iAMPA ratio (Wang,Gao,200). For ratio 0.9 use: 0.16/For ratio 1.3 use:0.25
ampaweight=	0.000058		//Pyramidal to pyramidal AMPA conductance (Wang,Gao,200).
ampaweightin=	7.5e-4			//According to Wang,Gao,2009
nmdaweightin=	3.2e-4			//Ratio inmda/iampa for interneuron *0.5(Wang,Gao,2009)
gabaaweight=	6.9e-4 			//According to Woo(2007)
gababweight=    1.05e-4			

bc_inh=106				//Number of spontaneously activated excitatory synapses of the interneuron model. Number of synapses=2 synapses*length.
maxsynpi=2				//PC-IN connections (Thomson, 2007)
maxsynip=4				//IN-PC connections (Peters,2008,Tamas,1997,J.Physiology,Perez,Larkum,2006)
syn_basal_con=5

perc_back_ap=1				//All synapses (2synapses*length) are spontaneously active along the apical tree  
perc_back_bas=1				//All synapses (2synapses*length) are spontaneously active along the basal tree  
s_Hz=0.1				//Frequency of poisson process

//Make directory to save data
strdef data_dir,tempstr
objref vsoma[nPcells], somaref[nPcells], vinsoma, insomaref
sprint(data_dir, "coincidence/back_microcirc_scaled_original/cl%d_int%d",cl_id,interval_t)
sprint(tempstr,"mkdir -p %s", data_dir) 
system(tempstr)	

//---------------------------------------------------RUN SIMULATION----------------------------------------------------//
//connect pyramidal neurons
connect_pyrb(num_run)

//connecte pyramidal neurons with interneuron
connect_pyrin(num_run)

//Include to simulate spontaneous input
//background(num_run,s_Hz) 

//Stimulation
time_stim(nPcells,interval_t,num_run)

//Record and save data
for cn=0, nPcells-1 {
	vsoma[cn] = new Vector()
	vsoma[cn].record(&Pcells[cn].soma[0].v(0.5))
}
vinsoma=new Vector()
vinsoma.record(&INcell[0].soma.v(0.5))
	
run()

for cn=0, nPcells-1 {
	somaref[cn] = new File()
	sprint(tempstr, "%s/soma_%d_run_%d.dat",data_dir, cn, num_run)
	somaref[cn].wopen(tempstr)
	vsoma[cn].printf(somaref[cn], "%f\n")
	somaref[cn].close()
}
insomaref = new File()
sprint(tempstr, "%s/insoma_run_%d.dat",data_dir, num_run)
insomaref.wopen(tempstr)
vinsoma.printf(insomaref, "%f\n")
insomaref.close()