Synchrony by synapse location (McTavish et al. 2012)

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Accession:144054
This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells.
Reference:
1 . McTavish TS, Migliore M, Shepherd GM, Hines ML (2012) Mitral cell spike synchrony modulated by dendrodendritic synapse location. Front Comput Neurosci 6:3 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): GabaB; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Synchronization; Olfaction;
Implementer(s): McTavish, Thomas S [thomas.mctavish at yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell; GabaB; AMPA; NMDA; I Na,t; I A; I K;
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mctavish_syncbylocation
src
ampanmda.mod
fi.mod
kamt.mod
kdrmt.mod
naxn.mod
ThreshDetect.mod *
allsynhinton.hoc *
analysis.py
animtest.py
antest.ses *
bulbspikes.py
clear.hoc
connect.hoc
control.ses
default.hoc
granule.hoc *
hinton.hoc
init.hoc *
iterator.hoc *
lptiter.hoc
mgrs.hoc
michele_movie.hoc
mitral.hoc
mosinit.hoc
net.hoc
param.hoc
params.py
parinit.hoc
pattern.hoc
perfrun.hoc
show.hoc
showw.hoc
somesyn.hoc *
sortspike *
split.hoc
start.hoc
start.ses
stimodors.hoc
subset.hoc
subset_control.ses *
synweightsnapshot.py
viewspikes.hoc
viewspikes1.hoc
weight_movie.hoc
weightsave.hoc
                            
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 17 22:10:35 2010

@author: -
"""
import numpy
from enthought.mayavi import mlab

x, y = numpy.mgrid[0:3:1,0:3:1]
s = mlab.surf(x, y, numpy.asarray(x*0.1, 'd'))
#
for i in range(10):
    s.mlab_source.scalars = numpy.asarray(x*0.1*(i+1), 'd')
#
# Produce some nice data.
n_mer, n_long = 6, 11
pi = numpy.pi
dphi = pi/1000.0
phi = numpy.arange(0.0, 2*pi + 0.5*dphi, dphi, 'd')
mu = phi*n_mer
x = numpy.cos(mu)*(1+numpy.cos(n_long*mu/n_mer)*0.5)
y = numpy.sin(mu)*(1+numpy.cos(n_long*mu/n_mer)*0.5)
z = numpy.sin(n_long*mu/n_mer)*0.5

# View it.
l = mlab.plot3d(x, y, z, numpy.sin(mu), tube_radius=0.025, colormap='Spectral')

# Now animate the data.
ms = l.mlab_source
for i in range(10):
    x = numpy.cos(mu)*(1+numpy.cos(n_long*mu/n_mer +
                                    numpy.pi*(i+1)/5.)*0.5)
    scalars = numpy.sin(mu + numpy.pi*(i+1)/5)
    ms.set(x=x, scalars=scalars)