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
        KM channel
   
	Author: A. Fontana
	CoAuthor: T.Nieus Last revised: 20.11.99
	
ENDCOMMENT
 
NEURON { 
	SUFFIX GRC_KM 
	USEION k READ ek WRITE ik 
	RANGE gkbar, ik, g, alpha_n, beta_n 
	RANGE Aalpha_n, Kalpha_n, V0alpha_n
	RANGE Abeta_n, Kbeta_n, V0beta_n
	RANGE V0_ninf, B_ninf
	RANGE n_inf, tau_n 
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	Aalpha_n = 0.0033 (/ms)
	Kalpha_n = 40 (mV)

	V0alpha_n = -30 (mV)
	Abeta_n = 0.0033 (/ms)
	Kbeta_n = -20 (mV)

	V0beta_n = -30 (mV)
	V0_ninf = -35 (mV)	:-30
	B_ninf = 6 (mV)		:6:4 rimesso a 6 dopo calibrazione febbraio 2003	
	v (mV) 
	gkbar= 0.00025 (mho/cm2) :0.0001
	ek = -84.69 (mV) 
	celsius = 30 (degC) 
} 

STATE { 
	n 
} 

ASSIGNED { 
	ik (mA/cm2) 
	n_inf 
	tau_n (ms) 
	g (mho/cm2) 
	alpha_n (/ms) 
	beta_n (/ms) 
} 
 
INITIAL { 
	rate(v) 
	n = n_inf 
} 
 
BREAKPOINT { 
	SOLVE states METHOD derivimplicit 
	g = gkbar*n 
	ik = g*(v - ek) 
	alpha_n = alp_n(v) 
	beta_n = bet_n(v) 
} 
 
DERIVATIVE states { 
	rate(v) 
	n' =(n_inf - n)/tau_n 
} 
 
FUNCTION alp_n(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-22(degC))/10(degC)) 
	alp_n = Q10*Aalpha_n*exp((v-V0alpha_n)/Kalpha_n) 
} 
 
FUNCTION bet_n(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-22(degC))/10(degC)) 
	bet_n = Q10*Abeta_n*exp((v-V0beta_n)/Kbeta_n) 
} 
 
PROCEDURE rate(v (mV)) {LOCAL a_n, b_n 
	TABLE n_inf, tau_n 
	DEPEND Aalpha_n, Kalpha_n, V0alpha_n, 
	       Abeta_n, Kbeta_n, V0beta_n, V0_ninf, B_ninf, celsius FROM -100 TO 30 WITH 13000 
	a_n = alp_n(v)  
	b_n = bet_n(v) 
	tau_n = 1/(a_n + b_n) 
:	n_inf = a_n/(a_n + b_n) 
	n_inf = 1/(1+exp(-(v-V0_ninf)/B_ninf))
} 

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