Biologically-Detailed Network Modelling - Computational Neuroscience: A Comprehensive Approach - page 284

Chapter 10

Biologically-DetailedNetwork
Modelling

Andrew Davison
Yale University School of Medicine, Section of Neurobiology, P.O. Box 208001, New
Haven, CT 06520-8001, U.S.

CONTENTS
10.1 Introduction
10.2 Cells
10.2.1 Modelling olfactory bulb neurons
10.2.2 Modelling cerebellum neurons
10.3 Synapses
10.4 Connections
10.4.1 Network topology
10.4.2 Numberofconnections
10.4.3 Distributionofconnections
10.5 Inputs
10.5.1 Spatiotemporal pattern of inputs
10.5.2 Inputs to individual cells
10.6 Implementation
10.7 Validation
10.8 Conclusions
References

10.1 Introduction

The appropriate level of detail for a computational neuroscience model is determined
by the nature of the system or phenomenon under investigation, by the amount of
experimental data available and by the aims of the investigator. For example, models
of certain network phenomena, e.g., synchronization, do not require details of cell
morphology, and perhaps not even ionic currents - integrate and fire neurons will
suffice. On the other hand, studying dendritic processing in active dendrites requires
details of dendritic morphology and of the active channels in the dendrites. However,
although a detailed model may be desirable, such a model must be well constrained