Optimization of neuronal morphologies for pattern recognition
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POSTER PRESENTATIONOpen Access
Optimization of neuronal morphologies for
Giseli de Sousa*, Reinoud Maex, Rod Adams, Neil Davey, Volker Steuber
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
San Antonio, TX, USA. 24-30 July 2010
Previous studies have shown that the morphology of a
neuron can affect its firing pattern [1,2]. Specifically,
some neuronal morphologies tend to favour bursting,
where short sequences of spikes are interspersed with
pauses in firing [1,2]. This type of bursting behaviour
has been observed in cerebellar Purkinje cells (PCs), and
previous work on associative memory in PCs has shown
that the generation of burst-pause sequences can be
important for information storage in the cerebellum .
These results have implications for the coding of infor-
mation in the brain, but they are specific to one particu-
lar neuron with a highly specialised morphology. In this
study we therefore use a general approach to optimise
generic neuronal structures for pattern recognition,
while analysing how their morphology influences their
To study how the ability of a neuron to perform pat-
tern recognition depends on morphology, we have built
a genomic representation of neuronal models, focusing
as a first objective on optimising dendritic architectures.
The optimization process uses an evolutionary algorithm
and involves four steps. Firstly, genotypes are generated,
which specify binary tree structures . Secondly, the
genotype is expressed as a model neuron phenotype, in
which the branching pattern is derived from the geno-
type, and which is then converted to a multi-compart-
mental model written in NEURON simulation code.
Thirdly, the fitness values are assessed by evaluating the
pattern recognition performance. Finally, genetic varia-
tion is introduced, using a process where the genes are
modified by crossover and mutation operators. Unlike
previous work that focussed on generating a subset of
realistic neuronal morphologies for specific computa-
tional tasks , our representation ensures that the
algorithm can generate the set of all possible morpholo-
gies for a specific number of terminal branches. The fit-
ness function evaluates pattern recognition performance
as described previously [3,6], by storing a number of
input patterns based on changing synaptic weights and
quantifying the ability of the model to distinguish the
set of stored patterns from a set of novel patterns. The
discrimination between stored and novel patterns is
evaluated for different features of the spike response
and quantified by calculating a signal-to-noise ratio. The
evolved artificial neuronal morphologies are compared
with reconstructed morphologies from real neurons. An
extension of the work involves optimizing other neuro-
nal features such as types and distributions of ion chan-
nels and the spatial structure of inputs in patterns.
Published: 20 July 2010
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Cite this article as: de Sousa et al.: Optimization of neuronal
morphologies for pattern recognition. BMC Neuroscience 2010
* Correspondence: email@example.com
Science and Technology Research Institute, University of Hertfordshire,
Hatfield, Herts, AL10 9AB, UK
de Sousa et al. BMC Neuroscience 2010, 11(Suppl 1):P80
© 2010 de Sousa et al; licensee BioMed Central Ltd.