A model for a nociceptor terminal and terminal tree (Barkai et al., 2020)

 Download zip file 
Help downloading and running models
Accession:266850
This model was used to study how the architecture of the nociceptor terminal tree affects the input-output relation of the primary nociceptive neurons. The model shows that the input-output properties of the nociceptive neurons depend on the length, the axial resistance, and location of individual terminals and that activation of multiple terminals by a capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output.
Reference:
1 . Barkai O, Butterman R, Katz B, Lev S, Binshtok AM (2020) The Input-Output Relation of Primary Nociceptive Neurons is Determined by the Morphology of the Peripheral Nociceptive Terminals. J Neurosci 40:9346-9363 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Dorsal Root Ganglion (DRG) cell;
Channel(s): I Potassium; I Calcium; I Sodium; I h;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Axonal Action Potentials; Nociception;
Implementer(s):
Search NeuronDB for information about:  I h; I Sodium; I Calcium; I Potassium; 
: nap.mod is a persistent Na+ current from
: Baker 2005, parameter assignments and formula's from page 854

NEURON {
	SUFFIX nap
	NONSPECIFIC_CURRENT i
	RANGE gbar, ena
}

UNITS {
	(S) = (siemens)
	(mV) = (millivolts)
	(mA) = (milliamp)
}

PARAMETER {
	gbar = 0.00005
	ena=60(mV)

	A_amp = 17.235 (/ms) : A for alpha m persis
	B_amp = 27.58 (mV)
	C_amp = -11.47 (mV)

	A_bmp = 17.235 (/ms) : A for beta m persis
	B_bmp = 86.2 (mV)
	C_bmp = 19.8 (mV)
}

ASSIGNED {
	v	(mV) : NEURON provides this
	i	(mA/cm2)
	g	(S/cm2)
	tau_m	(ms)
	minf
	hinf
}

STATE { m h }

BREAKPOINT {
	SOLVE states METHOD cnexp
	g = gbar * m^3
	i = g * (v-ena)
}

INITIAL {
	: assume that equilibrium has been reached
	m = alpham(v)/(alpham(v)+betam(v))
}

DERIVATIVE states {
	rates(v)
	m' = (minf - m)/tau_m
}

FUNCTION alpham(Vm (mV)) (/ms) {
	alpham=A_amp/(1+exp((Vm+B_amp)/C_amp))
}

FUNCTION betam(Vm (mV)) (/ms) {
	betam=A_bmp/(1+exp((Vm+B_bmp)/C_bmp))
}

FUNCTION rates(Vm (mV)) (/ms) {
	tau_m = 1.0 / (alpham(Vm) + betam(Vm))
	minf = alpham(Vm) * tau_m
}

Loading data, please wait...