  In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a
Differential Growing Neural Gas (DGNG) network architecture as a
computationally efficient way to model an Autoassociative Memory Cell (AMC)
\cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with
respect to possible applications in the field of computational neuroscience is
its \textit{capacity} refering to its capability to process and uniquely
distinguish input signals and therefore obtain a valid representation of the
input space. This study evaluates the capacity of a two layered DGNG grid cell
model on the Fashion-MNIST dataset. The focus on the study lies on the
variation of layer sizes to improve the understanding of capacity properties in
relation to network parameters as well as its scaling properties. Additionally,
parameter discussions and a plausability check with a pixel/segment variation
method are provided. It is concluded, that the DGNG model is able to obtain a
meaningful and plausible representation of the input space and to cope with the
complexity of the Fashion-MNIST dataset even at moderate layer sizes.
