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
8 - How to initialize simulations
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
For it is one of the more curious things about this Leviathan, that his skeleton gives very little idea of his general shape. Though Jeremy Bentham's skeleton, which hangs for candelabra in the library of one of his executors, correctly conveys the idea of a burly-browed utilitarian old gentleman, with all Jeremy's other leading personal characteristics; yet nothing of this kind could be inferred from any leviathan's articulated bones.
In most cases, initialization basically means the assignment of values at time t = 0 for membrane potential, gating states, and ionic concentrations at every spatial position in the model. A model is properly initialized when clicking on the Init & Run button produces exactly the same results, regardless of previous simulation history. Of course we assume that model parameters have not changed between runs, and that any random number generator has been re-initialized with the same seed so that it produces the same sequence of “random” numbers. Models described by kinetic schemes require that each of the reactant states be initialized to some concentration. If linear circuits are involved, initial values must be assigned to voltages across capacitors and the internal states of operational amplifiers. For networks and other models that use the event delivery system, initialization also includes specifying which events are in transit to their destinations at time 0 (i.e. events generated, at least conceptually, at t ≤ 0 for delivery at t ≥ 0).
- Type
- Chapter
- Information
- The NEURON Book , pp. 183 - 206Publisher: Cambridge University PressPrint publication year: 2006