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Greg Stuart, Nelson Spruston, and Michael Häusser

Print publication date: 2016

Print ISBN-13: 9780198745273

Published to Oxford Scholarship Online: May 2016

DOI: 10.1093/acprof:oso/9780198745273.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 18 January 2021

A theoretical view of the neuron as an input–output computing device

A theoretical view of the neuron as an input–output computing device

(p.439) Chapter 15 A theoretical view of the neuron as an input–output computing device

Etay Hay

Albert Gidon

Michael London

Idan Segev

Oxford University Press

Wilfrid Rall developed the theoretical foundations for understanding dendritic function almost 50 years ago. Since then his theory has been intensely employed and extended in order to explore the biophysical principles that link together the structure, physiology and function of dendrites. In this chapter we highlight some of the main insights that were gained from this theory. Based on these biophysical insights we highlight several key computations that electrically distributed dendrites (as opposed to a “point” neuron) could implement. The second half of the chapter demonstrates the first theme by reviewing a new experimentally-constrained approach to modeling dendritic/somatic nonlinearities, based on multiple objective optimization method combined with evolutionary algorithm. Recent insights about the role of dendritic inhibition obtained using theoretical and modeling approach are reviewed. Finally, additional emerging themes are discussed that are likely to become central in the near future in the theoretical study of dendrites. Models of dendrites and their synapses are essential for creating the bridge between the single-neuron level and the systems level.

Keywords:   Wilfrid Rall, modeling, theory, dendritic inhibition, biophysical principles, dendritic nonlinearities, single-neuron level, systems level

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