The Brain in a Box
An Encoding Scheme for Natural Neural Networks
Martin Pyka
1
, Tilo Kircher
1
, Sascha Hauke
2
and Dominik Heider
3
1
Section BrainImaging, Department of Psychiatry, University of Marburg, Marburg, Germany
2
Telecooperation Group, Technische Universit
¨
at Darmstadt, Darmstadt, Germany
3
Department of Bioinformatics, University of Duisburg-Essen, Duisburg and Essen, Germany
Keywords:
Neural Networks, Artificial Development, CPPN.
Abstract:
To study the evolution of complex nervous systems through artificial development, an encoding scheme for
modeling networks is needed that reflects intrinsic properties similiar to natural encodings. Like the ge-
netic code, a description language for simulations should indirectly encode networks, be stable but adaptable
through evolution and should encode functions of neural networks through architectural design as well as sin-
gle neuron configurations. We propose an indirect encoding scheme based on Compositional Pattern Produc-
ing Networks (CPPNs) to fulfill these needs. The encoding scheme uses CPPNs to generate multidimensional
patterns that represent the analog to protein distributions in the development of organisms. These patterns
form the template for three-dimensional neural networks, in which dendrite- and axon cones are placed in
space to determine the actual connections in a spiking neural network simulation.
1 INTRODUCTION
If evolutionary development, i.e., the stepwise im-
provement through mutation and crossover, is seen as
a means of understanding natural networks, two open
questions in particular present themselves. First, how
must a description language for neural networks (e.g.
complex nervous systems) look like? And second,
what are appropriate fitness functions to evolve com-
plex networks? In this article, we propose an answer
to the first question.
Because such a description language has to be ca-
pable of developing natural neural networks, it is im-
perative to know the intrinsic properties of the net-
works that should be modeled. In neuroscientific dis-
ciplines, numerous properties of natural neural net-
works are well-accepted that, in our opinion, should
receive considerably more attention in those disci-
plines that try to model networks by evolutionary
means.
For instance, the cortex of the brain consists of
layers of different neuron types interconnected with
each other. The connections are a side-effect of the
position of the neurons within the layer, their anatom-
ical form and their intersection with other neurons
(Wolpert, 2001). Connections between neurons can
be either specific (a clear and precise determination
of connections, e.g. as it can be found in the thalamus
(Basso et al., 2005)) or driven by side effects (e.g.
adjacent regions are interconnected with each other).
Furthermore, the (genetic) encoding scheme for
natural organisms itself has certain properties that
should be considered in simulations. In biological
organisms, comparatively few genes (e.g., the hu-
man genome consists of circa 22,000 genes) encode
complex structures by using highly indirect mecha-
nisms. The encoding involves local interactions be-
tween cells, diffusion of substrates and gene regu-
latory networks. One purpose of these mechanisms
is to generate global patterns from local interactions
between genes that serve as axes for the organiza-
tion and refinement of (cellular) structures (Mein-
hardt, 2008; Raff, 1996; Curtis et al., 1995). From this
notion, it can be concluded that an encoding scheme
for the evolutionary exploration of neural networks
should work in a highly indirect manner as well and
serve as a pattern generator for subsequent structural
elements. If the network architectures should be im-
provable via evolutionary mechanisms, the encoding
scheme must fulfill requirements for generating stable
network architectures across generations, but should
also facilitate alterations in the network that can im-
pact (and improve) their functionality.
In the following, we propose an encoding scheme,
196
Pyka M., Kircher T., Hauke S. and Heider D..
The Brain in a Box - An Encoding Scheme for Natural Neural Networks.
DOI: 10.5220/0004152801960201
In Proceedings of the 4th International Joint Conference on Computational Intelligence (ECTA-2012), pages 196-201
ISBN: 978-989-8565-33-4
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)