Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)

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Accession:135903
"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. ..."
Reference:
1 . Stacey WC, Krieger A, Litt B (2011) Network recruitment to coherent oscillations in a hippocampal computer model. J Neurophysiol 105:1464-81 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Oscillations;
Implementer(s): Lazarewicz, Maciej [mlazarew at gmu.edu]; Stacey, William [wstacey at med.umich.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Hippocampus CA1 interneuron oriens alveus GABA cell; GabaA; AMPA; NMDA; I Na,t; I A; I K; I h;
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Recruitnet
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CA1ih.mod *
CA1ika.mod *
CA1ikdr.mod *
CA1ina.mod *
caolmw.mod *
capr.mod *
Edrive.mod *
Exp2SynAMPA.mod *
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Exp2SynNMDA.mod *
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fvpre.mod *
gap.mod *
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na3n.mod *
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nafolmkop.mod *
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par_ggap.mod *
aux_fun.inc *
                            
COMMENT

//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//
// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
//
// Copyright 2007, The University Of Pennsylvania
// 	School of Engineering & Applied Science.
//   All rights reserved.
//   For research use only; commercial use prohibited.
//   Distribution without permission of Maciej T. Lazarewicz not permitted.
//   mlazarew@seas.upenn.edu
//
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

ENDCOMMENT

NEURON {
	SUFFIX Nafbwb
	USEION na WRITE ina
	GLOBAL phih
}
	
UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(mS) = (millisiemens)
}

PARAMETER {
    gna  = 35 (mS/cm2)
    ena  = 55 (mV)
    phih = 5
}
    
ASSIGNED {
    v       (mV)
    ina     (mA/cm2)
    minf    (1)
    hinf    (1)
    taoh    (ms)
	celsius (degC)
}

STATE { h }

INITIAL {
    rates(v)
    h    = hinf
}

BREAKPOINT {

	SOLVE states METHOD cnexp
	
	ina = (1e-3) * gna * minf^3 * h * (v-ena)
}

DERIVATIVE states { 
    rates(v)
    h' = (hinf-h)/taoh
}

PROCEDURE rates(v(mV)) { LOCAL am, bm, ah, bh, q10
    
    q10  = phih:^((celsius-27.0(degC))/10.0(degC))	
    
    am   = fun3(v,  -35, -0.1,    -10)
    bm   = fun1(v,  -60,  4,      -18) 
    minf = am/(am+bm)
 
    ah   = fun1(v,  -58,    0.07,  -20)
    bh   = fun2(v,  -28,    1,     -10)
    hinf = ah/(ah+bh)
    taoh = 1./((ah+bh)*q10)
}

INCLUDE "aux_fun.inc"

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