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10 - Synaptic transmission and artificial spiking cells

Published online by Cambridge University Press:  01 September 2010

Nicholas T. Carnevale
Affiliation:
Yale University, Connecticut
Michael L. Hines
Affiliation:
Yale University, Connecticut
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Summary

Though the certainty of this criterion is far from demonstrable, yet it has the savor of analogical probability.

In NEURON, a cell model is a set of differential equations. Network models consist of cell models and the connections between them. Some forms of communication between cells, e.g. graded synapses, gap junctions, and ephaptic interactions, require more or less complete representations of the underlying biophysical mechanisms. In these cases, coupling between cells is achieved by adding terms that refer to one cell's variables into equations that belong to a different cell. The first part of this chapter describes the POINTER syntax that makes this possible in NEURON.

The same approach can be used for detailed mechanistic models of spike-triggered transmission, which entails spike initiation and propagation to the presynaptic terminal, transmitter release, ligand–receptor interactions on the postsynaptic cell, and somatodendritic integration. However, it is far more efficient to use the widespread practice of treating spike propagation from the trigger zone to the synapse as a delayed logical event. The second part of this chapter tells how the NetCon (network connection) class supports this event-based style of communication.

In the last part of this chapter, we use event-based communication to simplify the representations of neurons themselves, creating highly efficient implementations of artificial spiking cells; for example, integrate and fire “neurons.”

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The NEURON Book , pp. 265 - 305
Publisher: Cambridge University Press
Print publication year: 2006

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