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• The state of the model – including the information
that is sent at a particular time – has an explicit
representation. This facilitates the creation and
application of measures that describe global, dy-
namic properties of the system enabling the com-
parison of systems that would otherwise be too
different to be compared in a more direct way.
• The model focuses on the interactions between
its information processing entities. The building
blocks of the model are designed such that the
modeling of complex interactions is facilitated.
One aspect of this is the ability of messages to ex-
ist over arbitrary long time intervals which, e.g.,
enables modeling of the spatial propagation of a
message.
In our future work we will use the presented model
to investigate phenomena observed in neuronal infor-
mation processing systems as they are a prime exam-
ple of complex systems. Their operation involves a
substantial variety of mechanisms, e.g., fast signal-
ing through action potentials, complex integration of
signals due to dendrite morphology and electrotonic
properties, change of processing characteristics via
neuromodulators, or the constant adjustment of net-
work topology by means of synaptic plasticity (Koch,
2004; Shepherd and Grillner, 2010). Furthermore,
nervous systems develop by evolutionary processes
over millions of years and are able to “bootstrap”
themselves by a process of self-organized growth
within a timescale of weeks to months (Butler and
Hodos, 2005; Squire et al., 2008).
Among the first phenomena that we want to anal-
yse are the mechanisms of self-organization utilized
during nervous system development such as biochem-
ical guidance cues which are used in the context of
neuron migration and axonal path finding.
We think that modeling and simulation of these
neuronal systems will not only result in a better un-
derstanding of the particular processes themselves but
may provide also insight into more general principles
of self-organization and emergence.
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