Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)

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Accession:139883
The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
Reference:
1 . Diwakar S, Lombardo P, Solinas S, Naldi G, D'Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control PLoS ONE 6(7):e21928
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum interneuron granule cell;
Channel(s): I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Octave;
Model Concept(s): Extracellular Fields; Evoked LFP;
Implementer(s): Diwakar, Shyam [shyam at amrita.edu];
Search NeuronDB for information about:  Cerebellum interneuron granule cell; GabaA; AMPA; NMDA; I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
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ReConv
data
readme.html
AmpaCOD.mod *
GRC_CA.mod *
GRC_CALC.mod *
GRC_GABA.mod *
GRC_KA.mod *
GRC_KCA.mod *
GRC_KIR.mod *
GRC_KM.mod *
GRC_KV.mod *
GRC_LKG1.mod *
GRC_LKG2.mod *
GRC_NA.mod *
NmdaS.mod *
Pregen.mod *
ComPanel.hoc
Grc_Cell.hoc
mosinit.hoc
Parametri.hoc
ReConv_GrC.jpg
ReConv_invitro.jpg
ReConv_invivo.jpg
Record_vext.hoc
Start.hoc
                            
TITLE Cerebellum Granule Cell Model

COMMENT
Based on Raman 13 state model. Adapted from Magistretti et al, 2006.
ENDCOMMENT

NEURON {
	SUFFIX GRC_NA
	USEION na READ ena WRITE ina
	RANGE gnabar, ina, g
	RANGE alfa, beta, gamma, delta, epsilon, teta, Con, Coff, Oon, Ooff
	RANGE Aalfa, Valfa, Abeta, Vbeta, Ateta, Vteta, Agamma, Adelta, Aepsilon, ACon, ACoff, AOon, AOoff
	RANGE n1,n2,n3,n4
}

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

PARAMETER {
	v (mV)
	celsius = 20  	(degC)
	ena = 87.39		(mV)
	gnabar = 0.013	(mho/cm2)
	Aalfa = 353.91 ( /ms)
	Valfa = 13.99 ( /mV) 
	Abeta = 1.272  ( /ms)
	Vbeta = 13.99 ( /mV)
	Agamma = 150 ( /ms)
	Adelta = 40  ( /ms)
	Aepsilon = 1.75 ( /ms)
	Ateta = 0.0201 ( /ms)
	Vteta = 25
	ACon = 0.005    ( /ms)
	ACoff = 0.5     ( /ms)
	AOon = 0.75     ( /ms)
	AOoff = 0.005   ( /ms)
	n1 = 5.422
	n2 = 3.279
	n3 = 1.83
	n4 = 0.738
}

ASSIGNED {
	ina  (mA/cm2)
	g   (mho/cm2)
	
	gamma
	delta
	epsilon
	Con
	Coff
	Oon
	Ooff
	a
	b
	Q10
	
}

STATE {
	C1
	C2
	C3
	C4
	C5
	O
	OB
	I1
	I2
	I3
	I4
	I5
	I6
}


INITIAL {
	C1=1
	C2=0
	C3=0
	C4=0
	C5=0
	O=0
	OB=0
	I1=0
	I2=0
	I3=0
	I4=0
	I5=0
	I6=0
	Q10 =3^((celsius-20(degC))/10 (degC))
	gamma = Q10 * Agamma
	delta = Q10 * Adelta
	epsilon = Q10 * Aepsilon
	Con = Q10 * ACon
	Coff = Q10 * ACoff
	Oon = Q10 * AOon
	Ooff = Q10 * AOoff
	a = (Oon/Con)^0.25
	b = (Ooff/Coff)^0.25

}

BREAKPOINT {
	SOLVE kstates METHOD sparse
	g = gnabar * O	      	: (mho/cm2)
	ina = g * (v - ena)  	: (mA/cm2)
}


FUNCTION alfa(v(mV))(/ms){ 
	alfa = Q10*Aalfa*exp(v/Valfa) 
}

FUNCTION beta(v(mV))(/ms){ 
	beta = Q10*Abeta*exp(-v/Vbeta) 
}

FUNCTION teta(v(mV))(/ms){ 
	teta = Q10*Ateta*exp(-v/Vteta) 
}
 

KINETIC kstates {
	: 1 riga
	~ C1 <-> C2 (n1*alfa(v),n4*beta(v))
	~ C2 <-> C3 (n2*alfa(v),n3*beta(v))
	~ C3 <-> C4 (n3*alfa(v),n2*beta(v))
	~ C4 <-> C5 (n4*alfa(v),n1*beta(v))
	~ C5 <-> O  (gamma,delta)
	~  O <-> OB (epsilon,teta(v))
	
	: 2 riga
	~ I1 <-> I2	(n1*alfa(v)*a,n4*beta(v)*b)
	~ I2 <-> I3	(n2*alfa(v)*a,n3*beta(v)*b)
	~ I3 <-> I4	(n3*alfa(v)*a,n2*beta(v)*b)
	~ I4 <-> I5 (n4*alfa(v)*a,n1*beta(v)*b)
	~ I5 <-> I6 (gamma,delta)
	
	: connette 1 riga con 2 riga
	~ C1 <-> I1 (Con,Coff)
	~ C2 <-> I2 (Con*a,Coff*b)
	~ C3 <-> I3 (Con*a^2,Coff*b^2)
	~ C4 <-> I4 (Con*a^3,Coff*b^3)
	~ C5 <-> I5 (Con*a^4,Coff*b^4)
	~  O <-> I6 (Oon,Ooff)
	
	CONSERVE C1+C2+C3+C4+C5+O+OB+I1+I2+I3+I4+I5+I6=1
}


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