Inferring connection proximity in electrically coupled networks (Cali et al. 2007)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:94321
In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods. Key words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance.
Reference:
1 . Calì C, Berger TK, Pignatelli M, Carleton A, Markram H, Giugliano M (2008) Inferring connection proximity in networks of electrically coupled cells by subthreshold frequency response analysis. J Comput Neurosci 24:330-45 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Glia;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Na,t; I K; I Potassium;
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB; PSpice; Sspice Symbolic SPICE;
Model Concept(s): Methods;
Implementer(s): Giugliano, Michele [mgiugliano at gmail.com];
Search NeuronDB for information about:  I Na,t; I K; I Potassium;
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
:

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)
	b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	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)
	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)
}

UNITSON

Loading data, please wait...