Balance of excitation and inhibition (Carvalho and Buonomano 2009)

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Accession:125689
" ... Here, theoretical analyses reveal that excitatory synaptic strength controls the threshold of the neuronal input-output function, while inhibitory plasticity alters the threshold and gain. Experimentally, changes in the balance of excitation and inhibition in CA1 pyramidal neurons also altered their input-output function as predicted by the model. These results support the existence of two functional modes of plasticity that can be used to optimize information processing: threshold and gain plasticity."
Reference:
1 . Carvalho TP, Buonomano DV (2009) Differential effects of excitatory and inhibitory plasticity on synaptically driven neuronal input-output functions. Neuron 61:774-85 [PubMed]
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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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Synaptic Integration;
Implementer(s):
// Loads extra PANEL with commonly changed variables




xpanel("Common_Parameters")
xbutton("MULTI()", "MULTI()")
xbutton("quit()", "quit()")

xlabel("Synaptic Weights")
xvalue("sExEx[ExINPUT][0].gmaxAMPA")
xvalue("sExInh[0][0].gmaxAMPA")
xvalue("sInhEx[ExINPUT][0].gmaxGABA")

xlabel("Run Control")
xvalue("numTrial")
xvalue("StoreEveryTrial")

xlabel("Network")
xvalue("totEx_Ex")
xvalue("totEx_Inh")
xvalue("totInh_Ex")
xvalue("AmpaMaxExEx")
xvalue("AmpaMaxExInh")
xvalue("GabaMax")
xvalue("AMPANMDARATIO_EPlasSyn")
xvalue("SetCaEx")
xvalue("SetCaInh")
//xvalue("ExNoise")
//xvalue("InhNoise")

xlabel("Plasticity")
xvalue("ScaleExEx")
xvalue("ScaleExInh")
//xvalue("ScaleInhEx")
xvalue("alfa_ScaleExEx")
xvalue("alfa_ScaleExInh")
//xvalue("STDP")
//xvalue("STDPINH")

xpanel(1100, 0)