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Understanding the complexity of biological neural
networks like the human brain is one of the scientific
challenges of our century. The organization of the brain
can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for
the description of functionally or effective connectivity are
based on the analysis of relations between the activities of
different neural units by detecting correlations or information
flow. This is a crucial step in understanding neural disorders
like Alzheimer’s disease and their causative factors.
To evaluate these estimation methods, it is necessary to refer
to a neural network with known connectivity, which is
typically unknown for natural biological neural networks.
Therefore, network simulations, also in silico, are available.
In this work, the in silico simulation of large scale neural
networks is established and the influence of different topologies
on the generated patterns of neuronal signals is investigated.
The goal is to develop standard evaluation methods
for neurocomputational algorithms with a realistic large
scale model to enable benchmarking and comparability of
different studies.