Model of peripheral nerve with ephaptic coupling (Capllonch-Juan & Sepulveda 2020)

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Accession:263988
We built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues is modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation.
Reference:
1 . Capllonch-Juan M, Sepulveda F (2020) Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves. PLoS Comput Biol 16:e1007826 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Axon;
Brain Region(s)/Organism:
Cell Type(s): Myelinated neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Ephaptic coupling; Stimulus selectivity;
Implementer(s):
/
publication_data
dataset_02__stimulation
ec
code
data
models
settings
src
x86_64
gaines_sensory_flut.mod *
gaines_sensory_mysa.mod *
gaines_sensory_node.mod *
gaines_sensory_stin.mod *
MRG_AXNODE.mod *
aaa_info_dataset *
algebra.py *
analysis.py *
anatomy.py *
biophysics.py *
circlepacker.py *
contourhandler.py *
electrodes.py *
fill_nerve.py *
geometry.py *
get_extstim.py *
read_results.py *
sim_launcher.py *
simcontrol.py *
tessellations.py *
tools.py *
visualisation.py *
workspace.py *
                            
TITLE Motor Axon MYSA channels

: 06/16
: Jessica Gaines
:
: Modification of channel properties
:
: 04/15
: Lane Heyboer
:
: Fast K+ current
: Ih current
:
: 02/02
: Cameron C. McIntyre
:
: Fast Na+, Persistant Na+, Slow K+, and Leakage currents 
: responsible for nodal action potential
: Iterative equations H-H notation rest = -80 mV
:
: This model is described in detail in:
: 
: Gaines JS, Finn KE, Slopsema JP, Heyboer LA, Polasek KH. A Model of 
: Motor and Sensory Axon Activation in the Median Nerve Using Surface 
: Electrical Stimulation. Journal of Computational Neuroscience, 2018.
:
: McIntyre CC, Richardson AG, and Grill WM. Modeling the excitability of
: mammalian nerve fibers: influence of afterpotentials on the recovery
: cycle. Journal of Neurophysiology 87:995-1006, 2002.

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

NEURON {
	SUFFIX gaines_sensory_mysa
	NONSPECIFIC_CURRENT ik
	NONSPECIFIC_CURRENT il
	NONSPECIFIC_CURRENT iq
	NONSPECIFIC_CURRENT ikf
	RANGE gkbar, gl, gq, gkf, ek, el, eq, ekf
	RANGE s_inf, q_inf, n_inf
	RANGE tau_s, tau_q, tau_n
}


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {

    : channel conductances
	gkbar   = 0.001324 (mho/cm2)
	gl	= 0.001716 (mho/cm2)
	gq	= 0.003102 (mho/cm2)
	gkf	= 0.1642 (mho/cm2)

    : reversal potentials
	ek      = -90.0 (mV)
	el	= -90.0 (mV)
	eq	= -54.9 (mV)
	ekf	= -90.0 (mV)

    : read in from .hoc file
	celsius		(degC)
	dt              (ms)
	v               (mV)
	vtraub=-80

    : parameters determining rate constants

    : slow K+
	asA = 0.3
	asB = -27
	asC = -5
	bsA = 0.03
	bsB = 10
	bsC = -1

    : HCN
	aqA = .00522
	aqB = -94.2
	aqC = -12.2
	bqA = .00522
	bqB = -94.2
	bqC = -12.2
   
    : fast K+
	anA = 0.0462
	anB = -83.2
	anC = 1.1
	bnA = 0.0824
	bnB = -66
	bnC = 10.5
}

STATE {
	s q n
}

ASSIGNED {
	ik      (mA/cm2)
	il      (mA/cm2)
	iq	(mA/cm2)
	ikf	(mA/cm2)
	s_inf
	q_inf
	n_inf
	tau_s
	tau_q
	tau_n
	q10_1
	q10_2
	q10_3
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ik   = gkbar * s * (v - ek)
	il   = gl * (v - el)
	iq = gq * q * (v-eq)
	ikf = gkf * n*n*n*n* (v-ekf)
}

DERIVATIVE states {   : exact Hodgkin-Huxley equations
       evaluate_fct(v)
	s' = (s_inf - s) / tau_s
	q' = (q_inf - q) / tau_q
	n' = (n_inf - n) / tau_n
}

UNITSOFF

INITIAL {
:
:	Q10 adjustment
:   Temperature dependence
:

	q10_1 = 2.2 ^ ((celsius-20)/ 10 )
	q10_2 = 2.9 ^ ((celsius-20)/ 10 )
	q10_3 = 3.0 ^ ((celsius-36)/ 10 )

	evaluate_fct(v)
	s = s_inf
	q = q_inf
	n = n_inf
}

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2

	v2 = v - vtraub : convert to traub convention

    : slow K+
	a = q10_3*asA / (Exp((v2+asB)/asC) + 1) 
	b = q10_3*bsA / (Exp((v2+bsB)/bsC) + 1)
	tau_s = 1 / (a + b)
	s_inf = a / (a + b)

    : HCN
	a = q10_3*aqA * (Exp((v-aqB)/aqC)) 
	b = q10_3*bqA / (Exp((v-bqB)/bqC))
	tau_q = 1 / (a + b)
	q_inf = a / (a + b)

    : fast K+
	a = q10_3*vtrapNA(v)
	b = q10_3*vtrapNB(v)
	tau_n = 1 / (a + b)
	n_inf = a / (a + b)
}

: vtrap functions to prevent discontinuity
FUNCTION vtrapNA(x){
    if(fabs((anB - x)/anC) < 1e-6){
        vtrapNA = anA*anC
    }else{
        vtrapNA = anA*(v-anB)/(1-Exp((anB-v)/anC))
    }
}

FUNCTION vtrapNB(x){
    if(fabs((x - bnB)/bnC) < 1e-6){
        vtrapNB = bnA*bnC  
    }else{
        vtrapNB = bnA*(bnB-v)/(1-Exp((v-bnB)/bnC))
    }
}

FUNCTION Exp(x) {
	if (x < -100) {
		Exp = 0
	}else{
		Exp = exp(x)
	}
}

UNITSON

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