## Principles of Computational Modelling in NeuroscienceThe nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience. |

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### Contents

13 | |

The HodgkinHuxley model of | 47 |

Compartmental models | 72 |

Intracellular mechanisms | 133 |

The synapse | 172 |

Simplified models of neurons | 196 |

The development of the nervous system | 267 |

### Other editions - View all

Principles of Computational Modelling in Neuroscience David Sterratt,Bruce Graham,Andrew Gillies,David Willshaw No preview available - 2011 |

### Common terms and phrases

action potential activity algorithm axon behaviour branch buffer Ca2+ cable calcium concentration calculated capacitance Chapter chemical synapses circuit compartment compartmental models complex conductance connections current flowing current injection curve dendrite density depends described diameter diffusion distribution dynamics electrical electrode etal example excitatory experimental exponential firing rate flux function gating particles GHK current equation gradient HH model Hodgkin and Huxley Hodgkin–Huxley I–V characteristic inactivation independent gating inhibitory injected current integrate-and-fire neuron intracellular ion channels ionic current kinetic schemes mathematical membrane potential molecules morphology neural neurite neuroscience parameter values passive pattern postsynaptic potassium channels presynaptic pyramidal cells rate coefficients RC circuit reaction receptors release response retinal Section shown in Figure signalling simulation single sodium soma spatial specific spike squid giant axon stochastic submembrane synapses synaptic strength tectum threshold time-step tion types vesicle voltage clamp Willshaw