    This is the readme for the model associated with the paper:

    Kim, J. K. & Fiorillo, C. D. Theory of optimal balance predicts and
    explains the amplitude and decay time of synaptic
    inhibition. Nat. Commun. 8, 14566 doi: 10.1038/ncomms14566 (2017).

    This model was contributed by Jaekyung Kim.

    This is a single-compartment model of a generic neuron. It was edited
    from the single-compartment model of thalamocortical (TC) relay
    neurons used in Destexhe et al. (1998). It includes passive (leakage),
    excitatory synaptic (AMPAergic), inhibitory synaptic (GABAergic or
    glycinergic), sodium and potassium (for action potential)
    conductances. It was used to test optimal parameters of synaptic
    inhibition.

    Two "~.hoc" files provide voltage responses for the EPSG ensemble of
    mean rate of 50 Hz, with parameters of IPSG before learning and after
    learning by learning rule 2 shown in Fig. 8 in Kim and Fiorillo
    (2017).

    This model was developed in Neuron 7.3.

      PROGRAMS and FILES
      ==================

    Before_learning.hoc : Voltage response with default parameters of
                          synaptic inhibition.
    After_learning.hoc  : Voltage response with default parameters learned
    		      from anti-Hebbian learnng rule (Rule 2 of
                          Fig. 8 in Kim and Fiorillo (2017)).
    IEIs_50Hz.tmp       : Randomized inter-EPSG interval simulated in Kim
                          and Fiorillo (2017).
    tc1.geo             : Geometry for single-compartment

      MECHANISMS
      ==========
    hh2.mod             : Fast spikes (Na+ and K+ currents)
    netstims.mod        : Presynaptic spike generator

      HOW TO RUN
      ==========
    Either auto-launch from ModelDB or:
    After compiling the mod files and starting the simulation with "~.hoc" files.
    For additional help see:
    https://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html

    If starting from mosinit.hoc or init.hoc choose a simulation by
    clicking a button.  Before learning generates these graphs:

    After learning generates these graphs:

    For further information, please contact Jaekyung Kim (kimjack0@kaist.ac.kr)