## An efficient method for computing synaptic conductances based on a kinetic
model of receptor binding

#### Alain Destexhe, Zachary F. Mainen and Terrence J. Sejnowski

Neural Computation 6: 14-18, 1994

## Abstract:

Reasonable biophysical assumptions about synaptic transmission allow the
equations for a simple kinetic synapse model to be solved analytically. This
yields a mechanism that preserves the advantages of kinetic models while being
as fast to compute as a single alpha -function. Moreover, this mechanism
accounts implicitly for saturation and summation of multiple synaptic events,
obviating the need for event queuing. The authors have presented a method by
which synaptic conductances can be computed with low computational expense.
The kinetic approach provides a natural means to describe the behavior of
synapses in a way that handles the interaction of successive presynaptic
events. Under the same assumption that transmitter concentration occurs as a
pulse, more complex kinetic schemes can be treated. The 'kinetic synapse' can
thus be generalized to give various conductance time courses with
multiexponential rise and decay phases, without sacrificing the efficiency of
the first-order model.

This tar file creates a directory containing a demo
for running the models of synaptic receptors using the Interviews version of
the NEURON simulator. The simulation reproduce the figures of the Neural Computation paper and the J. Comput. Neurosci. paper (above), in which
all details are given.
See also
SYN_NEW.zip. This package shows how to implement biophysical
models of synaptic interactions using NEURON. Both detailed and
simplified models of synaptic currents and most useful types of
postsynaptic receptors (AMPA, NMDA, GABA_A, GABA_B, neuromodulators)
are described in a reference paper. We provide here the complement
to simulate the same models using NEURON. The reference paper is a
chapter in the book "Methods in Neuronal Modeling":

Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.

Kinetic models of synaptic transmission.

In: * Methods in Neuronal
Modeling *, 2nd Edition, Edited by Koch, C. and Segev, I., MIT Press,
Cambridge, MA, 1998, pp. 1-25

in which all details are given. More instructions are provided
in a
README file.

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