Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)

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Accession:114359
The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes.
Reference:
1 . van Elburg RA, van Ooyen A (2010) Impact of dendritic size and dendritic topology on burst firing in pyramidal cells. PLoS Comput Biol 6:e1000781 [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: Neocortex;
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic cell;
Channel(s): I Na,t; I K; I M; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Activity Patterns; Bursting; Spatio-temporal Activity Patterns; Simplified Models; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Methods;
Implementer(s): van Elburg, Ronald A.J. [R.van.Elburg at ai.rug.nl];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic cell; I Na,t; I K; I M; I K,Ca; I Sodium; I Calcium; I Potassium;
COMMENT
alpha function synapse implemented as continuously integrated
kinetic scheme a la Srinivasan and Chiel (Neural Computation) so that
one can give many stimuli which summate.

Onset times are generated from exponentially decay distribution.

Conductance located in state variable G
The amplitude of each individual alpha function is given by stim,
stim * t * exp(-t/tau).
ENDCOMMENT

DEFINE SIZE 1000

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}


NEURON {
	POINT_PROCESS SynAlphaPoisson
	RANGE tau, stim, e, i,onset, offset, mean
	NONSPECIFIC_CURRENT i
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
}

PARAMETER {
	tau=.25 (ms)
	stim=50 (umho)
	e=0	(mV)
	v	(mV)
	mean  = 1000   (ms)
	onset  =0      (ms)
	offset =1e9    (ms)
}

ASSIGNED {
	index
	i (nA)
	bath (umho)
	k (/ms)
	t_activation (ms)
	interval (ms)
}

STATE {
	A (umho)
	G (umho)
}

INITIAL {
	k = 1/tau
	A = 0
	G = 0
	t_activation=t-1
	
	while( t > t_activation){
	   t_activation=onset-mean+mean*exprand(1)
	}
	net_send(t_activation,1)
}

? current
BREAKPOINT {
	SOLVE state METHOD sparse
	i = G*(v - e)
}

: at each onset time a fixed quantity of material is added to state A
: this material moves through G with the form of an alpha function



? kinetics
KINETIC state {
	~ A <-> G	(k, 0)
	~ G <-> bath	(k, 0)
}

NET_RECEIVE(w){
    if(flag==1){
        A = A + stim
        interval=mean*exprand(1)
        t_activation=t_activation+interval
        if(t_activation<offset){
            net_send(interval,1)
        }
    }
}
























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