Estimation of conductance in a conductance-based model of quadratic type (Vich & Guillamon 2015)

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We assume to have a quadratic approximation of a conductance-based neuron model, as in H.Rotstein (2015). Given the resulting membrane potential (v) and the course of the gating variable (w), this program estimates the synaptic current that the neuron is receiving at each time.    Moreover, given the voltage traces for two different applied (steady) currents and the excitatory and inhibitory reversal potentials, the program estimates the excitatory and inhibitory conductances separately.   Finally, the program gives the option of estimating the synaptic conductance. This conductance can be estimated in two different ways: (1) if only one voltage trace is given, the synaptic conductance is estimated using the synaptic reversal potential; (2) however, if two voltage traces are given (for two different applied currents), then the synaptic conductance can be either estimated using the synaptic reversal potential or the leak conductance.
1 . Vich C, Guillamon A (2015) Dissecting estimation of conductances in subthreshold regimes. J Comput Neurosci 39:271-87 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Entorhinal cortex stellate cell;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Parameter Fitting; Synaptic Integration; Conductances estimation;
Implementer(s): Vich, Catalina [catalina.vich at]; Guillamon, Antoni ;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell;
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