Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study
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Published version
Author(s)
Jarvis, Sarah
Nikolic, K
Schultz, SR
Type
Journal Article
Abstract
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron’s gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain modulation.
Date Issued
2018-03-09
Date Acceptance
2018-02-07
Citation
PLoS Computational Biology, 2018, 14 (3)
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
14
Issue
3
Copyright Statement
©
2018
Jarvis
et al. This is an open
access
article
distributed
under
the terms
of the
Creative
Commons
Attribution
License,
which
permits
unrestricte
d use, distribu
tion, and
reproduction
in any medium,
provided
the original
author
and source
are credited.
2018
Jarvis
et al. This is an open
access
article
distributed
under
the terms
of the
Creative
Commons
Attribution
License,
which
permits
unrestricte
d use, distribu
tion, and
reproduction
in any medium,
provided
the original
author
and source
are credited.
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Commission of the European Communities
Wellcome Trust
National Institutes of Health
Biotechnology and Biological Sciences Research Council (BBSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
BB/L018268/1
PIEF-GA-2013-628086
105603/Z/14/Z
UPMC: C15/0244
BB/K001817/1
EP/N002474/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
NEOCORTICAL PYRAMIDAL NEURONS
MEDIAL ENTORHINAL CORTEX
SHUNTING INHIBITION
SYNAPTIC INTEGRATION
CELLULAR MECHANISMS
FIRING RATE
IN-VIVO
CHANNELRHODOPSIN-2
MODULATION
EXCITATION
Computational Biology
Computer Simulation
Dendrites
Kinetics
Models, Neurological
Neurons
Opsins
Optogenetics
Pyramidal Cells
Synapses
Pyramidal Cells
Neurons
Dendrites
Synapses
Computational Biology
Kinetics
Models, Neurological
Computer Simulation
Opsins
Optogenetics
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status
Published
Article Number
ARTN e1006027