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)