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Chapter 4 - Compartmental models

Published online by Cambridge University Press:  05 June 2012

David Sterratt ,
Bruce Graham ,
Andrew Gillies  and
David Willshaw
David Sterratt
Affiliation:
University of Edinburgh
Bruce Graham
Affiliation:
University of Stirling
Andrew Gillies
Affiliation:
Psymetrix Limited, Edinburgh
David Willshaw
Affiliation:
University of Edinburgh
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Summary

In this chapter, we show how to model complex dendritic and axonal morphology using the multi-compartmental approach. We discuss how to represent an axon or a dendrite as a number of compartments derived from the real neurite's morphology. We discuss issues with measurement errors in experimentally determined morphologies and how to deal with them. Under certain assumptions, complex morphologies can be simplified for efficient modelling. We then consider how to match compartmental model output to physiological recordings and determine model parameters. We discuss in detail the techniques required for determining passive parameters, such as membrane resistance and capacitance over a distributed morphology. The extra problems that arise when modelling active membrane are also considered. Parameter estimation procedures are introduced.

Modelling the spatially distributed neuron

The basis of modelling the electrical properties of a neuron is the RC electrical circuit representation of passive membrane, consisting of a capacitor, leak resistor and a leak battery (Figure 2.14). Active membrane channels may be added by, for example, following the Hodgkin–Huxley approach. Further frameworks for modelling the myriad of ion channels found in neuronal membrane are covered in Chapter 5. If we are interested in voltage changes in more than just an isolated patch of membrane, we must consider how voltage spreads along the membrane. This can be modelled with multiple connected RC circuits. This approach is used widely and often referred to as multi-compartmental modelling or, more simply, compartmental modelling.

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Principles of Computational Modelling in Neuroscience , pp. 72 - 95
Publisher: Cambridge University Press
Print publication year: 2011

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