Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)

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Accession:3682
Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000 The original files for this model are located at the web site http://www-lnc.usc.edu/~karchie/synmap
Reference:
1 . Archie KA, Mel BW (2000) A model for intradendritic computation of binocular disparity. Nat Neurosci 3:54-63 [PubMed]
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): Neocortex V1 pyramidal corticothalamic L6 cell;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Spatio-temporal Activity Patterns; Active Dendrites;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Archie, Kevin A [karchie at lnc.usc.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; AMPA; I Na,t; I K;
This example produces a soma trajectory similar to that shown in fig 7a
of Archie, K.A. and Mel, B.W. A model of
intradendritic computation of binocular disparity
Nature Neuroscience 3:54-63, 2000.  Differences are due to synaptic noise.
Successive runs will produce different trajectories in response to randomly
computed synaptic event times.

The out.gif file shows the trajectory first obtained by launching the
mosinit.hoc file.

This example is based on the files from
http://www-lnc.usc.edu/~karchie/synmap/ but includes
a sample geom.Rin and grat_0_0.75.all.in
(generated from an optimal response grating)
file to allow autolaunch of the
example via the mosinit.hoc file. The changes to allow autolaunch consist
of moving the mod files from a subdirectory to the top level and a test
in hoc/synapses.hoc to allow automatic loading of the grat...in file.

The code which generates synaptic input files (not present in this
example but see the Readme_karchie file for a copy of the instructions
for that package) has been superseded by
the package at http://www-lnc.usc.edu/snv/
"SNV is a package for generating realistic spike trains in NEURON
simulations. Users create stimulus "movies," use linear-nonlinear cascade
models to generate the response of afferent neurons then map those
responses into synapses in specified locations on a cell."

Please contact karchie@lnc.usc.edu with questions about the paper, model
parameters, and usage of the snv system. Contact michael.hines@yale.edu
for questions about running this example.

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