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 [PubMed]
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
        Kir channel
   
	Author: E.D'Angelo, T.Nieus, A. Fontana
	Last revised: 8.10.2000
	Old values:
			gkbar = 0.0003 (mho/cm2) 
			
ENDCOMMENT
 
NEURON { 
	SUFFIX GRC_KIR
	USEION k READ ek WRITE ik 
	RANGE gkbar, ik, g, alpha_d, beta_d 
	RANGE Aalpha_d, Kalpha_d, V0alpha_d
	RANGE Abeta_d, Kbeta_d, V0beta_d
	RANGE d_inf, tau_d 
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	Aalpha_d = 0.13289 (/ms)

	:Kalpha_d = -0.041 (/mV)
	Kalpha_d = -24.3902 (mV)

	V0alpha_d = -83.94 (mV)
	Abeta_d = 0.16994 (/ms)

	:Kbeta_d = 0.028 (/mV)
	Kbeta_d = 35.714 (mV)

	V0beta_d = -83.94 (mV)
	v (mV) 
	gkbar = 0.0009 (mho/cm2) 
	ek = -84.69 (mV) 
	celsius = 30 (degC) 
} 

STATE { 
	d 
} 

ASSIGNED { 
	ik (mA/cm2) 
	d_inf 
	tau_d (ms) 
	g (mho/cm2) 
	alpha_d (/ms) 
	beta_d (/ms) 
} 
 
INITIAL { 
	rate(v) 
	d = d_inf 
} 
 
BREAKPOINT { 
	SOLVE states METHOD derivimplicit
	g = gkbar*d   : primo ordine!!!
	ik = g*(v - ek) 
	alpha_d = alp_d(v) 
	beta_d = bet_d(v) 
} 
 
DERIVATIVE states { 
	rate(v) 
	d' =(d_inf - d)/tau_d 
} 
 
FUNCTION alp_d(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-20(degC))/10(degC))
	alp_d = Q10*Aalpha_d*exp((v-V0alpha_d)/Kalpha_d) 
} 
 
FUNCTION bet_d(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-20(degC))/10(degC))
	bet_d = Q10*Abeta_d*exp((v-V0beta_d)/Kbeta_d) 
} 
 
PROCEDURE rate(v (mV)) {LOCAL a_d, b_d 
	TABLE d_inf, tau_d  
	DEPEND Aalpha_d, Kalpha_d, V0alpha_d, 
	       Abeta_d, Kbeta_d, V0beta_d, celsius FROM -100 TO 30 WITH 13000 
	a_d = alp_d(v)  
	b_d = bet_d(v) 
	tau_d = 1/(a_d + b_d) 
	d_inf = a_d/(a_d + b_d) 
} 


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