  Modern Machine learning techniques take advantage of the exponentially rising
calculation power in new generation processor units. Thus, the number of
parameters which are trained to resolve complex tasks was highly increased over
the last decades. However, still the networks fail - in contrast to our brain -
to develop general intelligence in the sense of being able to solve several
complex tasks with only one network architecture. This could be the case
because the brain is not a randomly initialized neural network, which has to be
trained by simply investing a lot of calculation power, but has from birth some
fixed hierarchical structure. To make progress in decoding the structural basis
of biological neural networks we here chose a bottom-up approach, where we
evolutionarily trained small neural networks in performing a maze task. This
simple maze task requires dynamical decision making with delayed rewards. We
were able to show that during the evolutionary optimization random severance of
connections lead to better generalization performance of the networks compared
to fully connected networks. We conclude that sparsity is a central property of
neural networks and should be considered for modern Machine learning
approaches.
