Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 A tour of the NEURON simulation environment
- 2 The modeling perspective
- 3 Expressing conceptual models in mathematical terms
- 4 Essentials of numerical methods for neural modeling
- 5 Representing neurons with a digital computer
- 6 How to build and use models of individual cells
- 7 How to control simulations
- 8 How to initialize simulations
- 9 How to expand NEURON's library of mechanisms
- 10 Synaptic transmission and artificial spiking cells
- 11 Modeling networks
- 12 hoc, NEURON's interpreter
- 13 Object-oriented programming
- 14 How to modify NEURON itself
- Appendix A1 Mathematical analysis of IntFire4
- Appendix A2 NEURON's built-in editor
- Epilogue
- Index
10 - Synaptic transmission and artificial spiking cells
Published online by Cambridge University Press: 01 September 2010
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 A tour of the NEURON simulation environment
- 2 The modeling perspective
- 3 Expressing conceptual models in mathematical terms
- 4 Essentials of numerical methods for neural modeling
- 5 Representing neurons with a digital computer
- 6 How to build and use models of individual cells
- 7 How to control simulations
- 8 How to initialize simulations
- 9 How to expand NEURON's library of mechanisms
- 10 Synaptic transmission and artificial spiking cells
- 11 Modeling networks
- 12 hoc, NEURON's interpreter
- 13 Object-oriented programming
- 14 How to modify NEURON itself
- Appendix A1 Mathematical analysis of IntFire4
- Appendix A2 NEURON's built-in editor
- Epilogue
- Index
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.”
- Type
- Chapter
- Information
- The NEURON Book , pp. 265 - 305Publisher: Cambridge University PressPrint publication year: 2006