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

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

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: 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

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Model Information (Click on a link to find other models with that property)

Model Type:	Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):	Cerebellar 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: Cerebellar granule cell; GabaA; AMPA; NMDA; I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;

Model files

Help downloading and running models


Reconstructing evoked LFP in cerebellar granular layer using jittered
repetitive convolution (ReConv)

This is the README for ReConv model for the paper 

Shyam Diwakar, Paola Lombardo, Sergio Solinas, Giovanni Naldi, Egidio
D'Angelo.  "Local field potential modeling predicts dense activation
in cerebellar granule cells clusters under LTP and LTD control", PLoS
ONE, 2011 6(7):e21928
 
Implementation was done by Shyam Diwakar in Neuron and Matlab. Author
acknowledges Harilal Parasuram for his help in editing the code to
autogenerate data traces.


Usage instructions:

Auto-launch from ModelDB or download and extract the archive.  Then
under:

----
MSWIN

run mknrndll, cd to the archive and make the nrnmech.dll.  Then double
click on the mosinit.hoc file.

When the "Control Panel" menu pops, click on invitro first and restart
Start.hoc and click on invivo menu to make data files.  Run
"weighsum.m" inside data/invitro and "weighavg.m" inside data/invivo
to generate evoked LFP signal.

----
MAC OS X

Drag and drop the GrC folder onto the mknrndll icon.  Drag and drop
the mosinit.hoc file onto the nrngui icon.

When the "Control Panel" menu pops, click on invitro first and restart
Start.hoc and click on invivo menu to make data files.  Run
"weighsum.m" inside data/invitro and "weighavg.m" inside data/invivo
to generate evoked LFP signal.
----
Linux/Unix

Change directory to the GrC folder. run nrnivmodl. Then type 
nrngui mosinit.hoc

When the "Control Panel" menu pops, click on invitro first and restart
Start.hoc and click on invivo menu to make data files.
Run "weighsum.m" inside data/invitro and "weighavg.m" inside
data/invivo to generate evoked LFP signal.
----
  


 
Attention: 

The GrC model used here was published as (Diwakar et al., 2009)

Shyam Diwakar, Jacopo Magistretti, Mitchell Goldfarb, Giovanni Naldi,
and Egidio D'Angelo. Axonal Na+ channels ensure fast spike activation
and back-propagation in cerebellar granule cells. J Neurophysiol
(December 10, 2008).  doi:10.1152/jn.90382.2008

Available at
http://senselab.med.yale.edu/ModelDb/showmodel.asp?model=116835

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