Development of a framework to simulate learning and task solving inspired by the hippocampus and successor representation

Type: MA thesis

Status: finished

Date: January 25, 2021 - July 26, 2021

Supervisors: Andreas Maier

Since nervous systems have developed very efficient
mechanisms to store, retrieve and even extrapolate from
learned experience, machine learning has always oriented
itself on nature. Even though the discovery of neural
networks, support vector machines and deep networks have
been significantly pushing performance, science is still far
away from completely understanding the brain’s
implementation of those phenomena.

The hippocampus is a structure of the brain present in both
hemispheres. It has been proven to be responsible for both
spatial orientation and memory management [1, 2] but recent
studies suggest it is involved in far more profound tasks of
learning. This new theory assumes the hippocampus creates
abstract cognitive maps with the ability to predict unknown
states, joining the proven findings already mentioned
above.[3] To further investigate and study this behaviour
and possibly add proof to the theory, it is crucial to
examine the two dominant neural cell types which have
already been identified in the context of spatial
orientation. These are so called place cells on the one hand
and grid cells on the other.

Place- and grid cells were originally discovered to encode
spatial information and thus named accordingly. According to
the theory of abstract cognitive mapping in the hippocampus,
place cells’ activities are believed to represent states
in general. Grid cells were originally discovered firing
uniformly over space in different orientations, generating
some kind of coordinate system. In the context of this more
holistic theory, grid cells could provide a reference frame
for the abstract cognitive map. Many experiments have been
conducted to investigate the behaviour of the hippocampuses
structures related to learning.

The aim of this thesis is to create a framework for
researchers to simulate and work in environments which are
held so simple that the results can be transferred to any
other cognitive map. Hopefully this can help to avoid
complicated experimental setups, the use of laboratory
animal experiments and speed up research on the
hippocampuses role in learning in the future.

[1] D. S. Olton, J. T. Becker, and G. E. Handelmann.
“Hippocampus, space, and memory”. In: Behavioral and
Brain Sciences 2.3 (1979), pp. 313–322. issn: 0140-525X.
doi: 10.1017/S0140525X00062713.

[2] B. Milner, S. Corkin, and H.-L. Teuber. “Further
analysis of the hippocampal amnesic syndrome: 14-year
follow-up study of HM”. In: Neuropsychologia 6.3 (1968),
pp. 215–234. issn: 0028-3932.

[3] K. L. Stachenfeld, M. M. Botvinick, and S. J. Gershman.
“The hippocampus as a predictive map”. In: Nature
neuroscience 20.11 (2017), p. 1643. issn: 1546-1726.