Kinetic models of synaptic transmission.
Alain Destexhe, Zachary F. Mainen and Terrence J. Sejnowski
In: Methods in Neuronal Modeling, 2nd Edition, Edited by Koch, C. and Segev, I., MIT Press, Cambridge, MA, 1998, pp. 1-25.
Copy of the full paper (PDF)
Abstract
This chapter reviews recent advances in the understanding of neurochemical transmission and efficient models to incorporate synaptic interactions in neuronal models. After a brief introduction on the different types of ion channels and their gating mechanisms, the first section focuses on the mechanisms of transmitter release by presynaptic terminals. A detailed model of the release process is presented, as well as a simplified representation of the properties of release. The second section reviews detailed models for the most prominent “fast” receptor types in the CNS (AMPA, NMDA, GABA_A) as well as neuromodulators (with GABA_B receptors as a prototype). The next section provides the reader with simplified representations of these currents that are fast to compute while capturing the most salient features of AMPA, NMDA, GABA_A and GABA_B receptor-mediated neurotransmission. For both detailed and simplified models, the kinetic models are compared to experimental data. The last section gives practical implementations of the simplified models, by illustrating the summation of synaptic events for the different types of receptors. An example is also presented for network simulations in which synaptic interactions are represented by kinetic models. Finally, the appendixes gives more formal details on the kinetic representation of gating mechanisms, the procedures used to fit the models to experimental data, and optimized algorithms to implement kinetic synapse models at low computational cost.
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 the reference paper (above). We provide here the complement to simulate the same models using NEURON. More instructions are provided in a README file.
|
