Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)

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Accession:3264
This archive contains 3 mod files for NEURON that implement the short term synaptic plasticity model described in Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., and Nelson, S.B.. A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience 17:7926-7940, 1997. Contact ted.carnevale@yale.edu if you have questions about this implementation of the model.
Reference:
1 . Varela JA, Sen K, Gibson J, Fost J, Abbott LF, Nelson SB (1997) A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. J Neurosci 17:7926-40 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Facilitation; Depression; Vision;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu];
Search NeuronDB for information about:  Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; AMPA; I Na,t; I K; Glutamate;
load_file("nrngui.hoc")
load_file("testgroup.ses")
load_file("runtest.ses")

objref vbox
vbox = new VBox()
vbox.intercept(1)       //all following creations go into the "vbox" box
xpanel("Comments")
xlabel("'Biophysical' cell with multiple input streams")
xlabel("Demonstration of efficient convergence and stream-specific use-dependent plasticity.")
xlabel("In NEURON, multiple input streams ('spike trains') can share a single postsynaptic")
xlabel("mechanism so that the conductance of the shared mechanism equals the total conductance")
xlabel("that would have occurred if each input stream had its own separate mechanism.  This is")
xlabel("computationally efficient because it means that only one set of equations has to be")
xlabel("solved to find the net effect of any number of input streams.  Furthermore, the")
xlabel("postsynaptic action of each input stream can show its own use-dependent plasticity.")
xpanel()
xpanel("Instructions")
xlabel("Postsynaptic C0 carries three postsynaptic conductance mechanisms, called")
xlabel("FDSExp2Syn[0], FDSExp2Syn[1], and FDSExp2Syn[2], that have identical biophysical")
xlabel("properties, including short-term plasticity.")
xlabel("Presynaptic Sfast1 fires at 100 ms intervals and drives FDSExp2Syn[0].")
xlabel("Presynaptic Sslow2 fires half as fast and drives FDSExp2Syn[1].")
xlabel("In addition, Sfast1 and Sslow2 both drive FDSExp2Syn[2].")
xlabel("That is, FDSExp2Syn[2] receives convergent input streams from different sources.")
xlabel("1.  Before examining the GUI tools used to make this net, run a simulation and")
xlabel("    verify that the blue trace stays on top of the x axis.  This shows that")
xlabel("    FDSExp2Syn[0].g + FDSExp2Syn[1].g - FDSExp2Syn[2].g = 0")
xlabel("2.  For fastest results, be sure to use 'Local variable dt'")
xlabel("    (see the VariableTimeStep window)")
xlabel("3.  Use")
xlabel("      NEURON Main Menu / Window / netstuff")
xlabel("    to reveal the GUI for this demo's network")
xpanel()
vbox.intercept(0)       //ends intercept mode 
vbox.map()              //draw the box and its contents