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Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994)
Home Page: https://modeldb.yale.edu/18197
This project forked from modeldbrepository/18197
Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994)
Home Page: https://modeldb.yale.edu/18197
Neural_Computation ------------------ Simmulations that illustrate the application of simple kinetic models for excitatory and inhibitory synaptic currents. These simulations are related to the following paper: Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Computation 6: 10-14, 1994. (see postscript file synapse.ps.Z) ( or pdf at http://cns.iaf.cnrs-gif.fr/abstracts/synapse.html ) This directory contains all the files needed to run the simulations using the Interviews version of NEURON. These files are commented and should run straighforwardly, provided the NEURON simulator is installed properly. The kinetic synapse mechanism ----------------------------- This mechanisms has the following properties: 1. It is based on a simple kinetic scheme of binding of transmitter on postsynaptic receptors. This description has the advantage that it is fully compatible with the level of description used for other mechanisms (Hodgkin-Huxley currents, calcium diffusion, etc). 2. The mechanism gives EPSP's or IPSP's from a pulse of transmitter. The waveform of these PSP's is very close to EPSP's or IPSP's measured experimentally, and the decay is monoexponential. The user can set all the parameters corresponding to the rising phase, decay, amplitude, etc... (see .mod files) 3. Summation of consecutive PSP's is handled automatically by the mechanism without need for an explicit event cue. 4. Each synapse has a state variable corresponding to the fraction of postsynaptic receptors in the open state. However, the kinetics are first order, and so can be solved exactly. This has the important advantage that it can be fit very easily to experimental recordings (see J. Computational Neurosci. paper). 5. Finally, this mechanism is very fast to compute. It does not require solving any differential equations; at any given time only one exponential is calculated per synapse. Thus, the mechanism is as fast to compute as optimized versions of alpha function-based models. How to run the simulation ------------------------- This directory contains the files necessary to run a simulation such as illustrated in Fig.1 of the Neural Computation paper. To compile the demo, NEURON and INTERVIEWS must be installed and working on the machine you are using. Just type "nrnivmodl" to compile the mechanisms given in the mod files (glutamate.mod and gaba.mod are the mechanisms for glutamate and gaba synapses, and HH.mod is the Hodgkin-Huxley kinetics). Then, execute one of the two example files by typing: special demo_glutamate_neuralcomputation.oc - or special demo_gaba_neuralcomputation.oc - Once the menu and graphics interface has appeared, click on "Init and Run" button to start the simulation... All these simulations were done using the NEURON simulator written by Michael Hines, and which is available freely on internet via anonymous ftp from neuron.neuro.duke.edu:/neuron. For more information about how to get NEURON and how to install it, please refer to the following sites: http://www.neuro.duke.edu/neuron/home.html http://www.nnc.yale.edu/HTML/YALE/NNC/neuron/neuron.html For further information, please contact: Alain Destexhe The Salk Institute Computational Neurobiology Laboratory 10010 North Torrey Pines Road La Jolla CA 92037, USA Department of Physiology Laval University Quebec G1K 7P4 Canada http://www.cnl.salk.edu/~alain/ email: [email protected]
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