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; 
//Two events stimulus with increasing inter-stimulus interval, to study coincidence detection
 
objref ns_t[2],nc_ampa_t[2][nPcells][500],nc_nmda_t[2][nPcells][500], ampa_t[2][nPcells][500], nmda_t[2][nPcells][500], mat_bas_t[nPcells]

proc time_stim() {
	num_neurons=$1
	interval_t=$2
	dens_t=0.2
	r=new Random($3*7)
	r.uniform(0,1)
	
	delay_stim=500

	for n_ns=0,1 {
		ns_t[n_ns]=new NetStim(0.5)
		ns_t[n_ns].interval=0
		ns_t[n_ns].number=1
		ns_t[n_ns].start=delay_stim
		ns_t[n_ns].noise=0
		
		for pn=0,num_neurons-1 {
			mat_bas_t[pn]=new Vector()
			tot_bas=0
			syn_basal_t=0
			forsec Pcells[pn].basal {tot_bas=tot_bas+L}	
			syn_basal_t=2*tot_bas*dens_t/2
			forsec Pcells[pn].basal {
				mat_bas_t[pn].append(int((L/tot_bas)*syn_basal_t))
			}
			syn=-1
			num=0
			forsec Pcells[pn].basal {
				for many_t=0, mat_bas_t[pn].x(num)-1 {
					syn=syn+1
					
					PID=r.repick()

					ampa_t[n_ns][pn][syn]=new GLU(PID)
					nmda_t[n_ns][pn][syn]=new nmda_spikes(PID)

					nc_ampa_t[n_ns][pn][syn] = new NetCon(ns_t[n_ns], ampa_t[n_ns][pn][syn], -20, 0, ampaweight)
					nc_nmda_t[n_ns][pn][syn] = new NetCon(ns_t[n_ns], nmda_t[n_ns][pn][syn], -20, 0, nmdaweight)
				}
				num=num+1
			}
		}
		delay_stim=delay_stim+interval_t
	}
}