Christina M Weaver

Danel Draguljić

Jennifer Luebke

Patrick Hof

Timothy Rumbell

BMC Neuroscience

PubMed

Springer - Publisher Connector

Automatic fitness function selection for compartment model optimization

ORAL PRESENTATION Open Access
Automatic fitness function selection for
compartment model optimization
Timothy Rumbell1*, Danel Draguljić2, Jennifer Luebke3, Patrick Hof1, Christina M Weaver2
From The Twenty Third Annual Computational Neuroscience Meeting: CNS*2014
Québec City, Canada. 26-31 July 2014
During normal aging, layer 3 pyramidal neurons of the
rhesus monkey prefrontal cortex (PFC) exhibit significant
morphological changes, as well as higher action potential
firing rates in vitro [1]. Computational modeling of indivi-
dual neurons can provide insight into the ionic mechan-
isms underlying the increased excitability, which are
currently unknown. A unique database of electrophysiolo-
gical recordings and morphologic reconstructions from
the same neurons, gathered through whole-cell patch
clamp recording, confocal microscopy and 3D digital tra-
cing, constrains the models. Initial modeling of six young
and six aged neurons demonstrated that morphological
features alone do not account entirely for the electrophy-
siological changes with aging [2]. It is now necessary to
explore the parameter space of passive cable properties
and active membrane channel conductances and kinetics,
to uncover parameter combinations that reproduce the fir-
ing patterns observed in neurons of each age group.
Differential Evolution (DE) is an evolutionary optimiza-
tion method capable of identifying a population of candi-
date models throughout parameter space that closely
match empirically observed firing patterns. The quality of
fit achieved by an optimization is reliant on the ‘fitness
functions’ used to measure the accuracy of the model.
Previous neuronal compartment modeling studies using
parameter optimization have introduced multiple types
of fitness measurement [3], but have not described a gen-
eral method to determine weights for each type. Here we
introduce a novel method for automatically establishing
weights of minimally correlated fitness functions, and
apply it to optimization of models of young and aged
PFC neurons. First, a Latin hypercube design (< 1000
points) provides a space-filling sampling of parameter
space; the candidate fitness functions are then evaluated
at each of these points. Second, clusters of fitness func-
tions that are highly correlated across the hypercube are
pruned to leave one representative member. Third, a
principal component analysis of the remaining fitness
functions across the hypercube identifies a set of fitness
functions representing most of the variability in the para-
meter space, which are selected for use in the optimiza-
tion. Fourth, weights for each selected fitness function
are calculated based on the combination of coefficients
for principal components and variance explained by
those components. Finally, DE is conducted on the Neu-
roscience Gateway [4] using this automatically con-
structed optimization protocol.
We demonstrate the method with a compartment
model comprising a simplified pyramidal neuron mor-
phology and three ion channels, optimized to data from
representative young and aged neurons. Compared to a
manual approach involving iterative generation of fitness
functions, our novel method produces better fitting mod-
els using a tenth of the computation time. Future work
will extend the automatic protocol generation to priori-
tize which parameters to optimize, a critical step as more
ion channels are added to the model to improve fitness.
This method will be used to generate morphologically
detailed models of 20+ young and aged PFC neurons,
predicting which ionic mechanisms underlie age-related
physiological changes.
Acknowledgements
This project was supported by NIH grants AG00001, AG025062, and
AG035071.
Authors’ details
1Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn
School of Medicine at Mount Sinai, New York, NY 10029. 2Department of
Mathematics, Franklin and Marshall College, Lancaster, PA, 17604.
3Department of Anatomy and Neurobiology, Boston University School of
Medicine, Boston, MA, 02118.
* Correspondence: timothy.rumbell@mssm.edu
1Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn
School of Medicine at Mount Sinai, New York, NY 10029
Full list of author information is available at the end of the article
Rumbell et al. BMC Neuroscience 2014, 15(Suppl 1):O5
http://www.biomedcentral.com/1471-2202/15/S1/O5
© 2014 Rumbell et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://
creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Published: 21 July 2014
References
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doi:10.1186/1471-2202-15-S1-O5
Cite this article as: Rumbell et al.: Automatic fitness function selection
for compartment model optimization. BMC Neuroscience 2014
15(Suppl 1):O5.
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Automatic fitness function selection for compartment model optimization

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