  Introduction: In contrast to current AI technology, natural intelligence --
the kind of autonomous intelligence that is realized in the brains of animals
and humans to attain in their natural environment goals defined by a repertoire
of innate behavioral schemata -- is far superior in terms of learning speed,
generalization capabilities, autonomy and creativity. How are these strengths,
by what means are ideas and imagination produced in natural neural networks?
  Methods: Reviewing the literature, we put forward the argument that both our
natural environment and the brain are of low complexity, that is, require for
their generation very little information and are consequently both highly
structured. We further argue that the structures of brain and natural
environment are closely related.
  Results: We propose that the structural regularity of the brain takes the
form of net fragments (self-organized network patterns) and that these serve as
the powerful inductive bias that enables the brain to learn quickly, generalize
from few examples and bridge the gap between abstractly defined general goals
and concrete situations.
  Conclusions: Our results have important bearings on open problems in
artificial neural network research.
