The human brain is a big role model for computer science. Many applications
like Neural Networks mimic brain functions with great success. However a lot of
functions are still not well understood and therefore subject to present research.
The hippocampus is one of the regions of greater interest. It is a central part
of memory processing, the limbic system and used for spatial navigation. Place
and grid cells are two important cell types found in the hippocampus, which
help to encode information for navigational tasks [1].
New theories however extend this view from spatial navigation to more abstract
navigation, which can be used for all concepts of information. In the paper The
hippocampus as a predictive map a mathematical description of the place cells
in the hippocampus, the Successor Representation (SR) is developed. The SR
can be used to imitate the data processing method of the hippocampus and
could already recreate experimental results [2]. Other experiments have also
extended the view from spatial navigation to broader information processing.
For example that the grid cells do not encode only the euclidean distances [3]
or that we use grid and place cells to orientate in our eld of vision [4]. All of
this could lead to powerful data processing tool, which can adept
exible to all
kinds of problems.
This thesis wants to build a framework which can be used to use and analyze
the properties of the SR. The framework should enable to create di erent envi-
ronments for simple navigation tasks, but also to get more abstract information
relationships in graphs. Furthermore mathematical properties should be ana-
lyzed to improve the learning process and to gain a broader understanding of
the functionality of the SR.
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