Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)

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Accession:223649
" ... We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. ..."
Reference:
1 . Alturki A, Feng F, Nair A, Guntu V, Nair SS (2016) Distinct current modules shape cellular dynamics in model neurons. Neuroscience 334:309-331 [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: Hippocampus; Amygdala;
Cell Type(s): Abstract single compartment conductance based cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Activity Patterns; Oscillations; Methods; Olfaction;
Implementer(s):
/
AlturkiEtAl2016
2_Pospischil
Original
README.html *
cadecay_destexhe.mod *
HH_traub.mod *
IL_gutnick.mod
IM_cortex.mod
IT_huguenard.mod
demo_IN_FS.hoc *
demo_PY_IB.hoc *
demo_PY_IBR.hoc *
demo_PY_LTS.hoc *
demo_PY_RS.hoc *
fig5b.jpg *
mosinit.hoc *
rundemo.hoc *
sIN_template *
sPY_template *
sPYb_template *
sPYbr_template *
sPYr_template *
                            
TITLE Hippocampal HH channels
:
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:
: Modified Oct 96 for compatibility with Windows: trap low values of arguments
:

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

NEURON {
	SUFFIX hh2
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub
	RANGE m_inf, h_inf, n_inf
	RANGE tau_m, tau_h, tau_n
	RANGE m_exp, h_exp, n_exp
}


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

PARAMETER {
	gnabar  = .003  (mho/cm2)
	gkbar   = .005  (mho/cm2)

	ena     = 50    (mV)
	ek      = -90   (mV)
	celsius = 36    (degC)
	dt              (ms)
	v               (mV)
	vtraub  = -63   (mV)
}

STATE {
	m h n
}

ASSIGNED {
	ina     (mA/cm2)
	ik      (mA/cm2)
	il      (mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}


BREAKPOINT {
	SOLVE states
	ina = gnabar * m*m*m*h * (v - ena)
	ik  = gkbar * n*n*n*n * (v - ek)
}


:DERIVATIVE states {   : exact Hodgkin-Huxley equations
:       evaluate_fct(v)
:       m' = (m_inf - m) / tau_m
:       h' = (h_inf - h) / tau_h
:       n' = (n_inf - n) / tau_n
:}

PROCEDURE states() {    : exact when v held constant
	evaluate_fct(v)
	m = m + m_exp * (m_inf - m)
	h = h + h_exp * (h_inf - h)
	n = n + n_exp * (n_inf - n)
	VERBATIM
	return 0;
	ENDVERBATIM
}

UNITSOFF
INITIAL {
	m = 0
	h = 0
	n = 0
:
:  Q10 was assumed to be 3 for both currents
:
: original measurements at roomtemperature?

	tadj = 3.0 ^ ((celsius-36)/ 10 )
}

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

	v2 = v - vtraub : convert to traub convention

:       a = 0.32 * (13-v2) / ( Exp((13-v2)/4) - 1)
	a = 0.32 * vtrap(13-v2, 4)
:       b = 0.28 * (v2-40) / ( Exp((v2-40)/5) - 1)
	b = 0.28 * vtrap(v2-40, 5)
	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	a = 0.128 * Exp((17-v2)/18)
	b = 4 / ( 1 + Exp((40-v2)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)

:       a = 0.032 * (15-v2) / ( Exp((15-v2)/5) - 1)
	a = 0.032 * vtrap(15-v2, 5)
	b = 0.5 * Exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)

	m_exp = 1 - Exp(-dt/tau_m)
	h_exp = 1 - Exp(-dt/tau_h)
	n_exp = 1 - Exp(-dt/tau_n)
}
FUNCTION vtrap(x,y) {
	if (fabs(x/y) < 1e-6) {
		vtrap = y*(1 - x/y/2)
	}else{
		vtrap = x/(Exp(x/y)-1)
	}
}

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

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