Speaker
Jochen Kerdels
(University of Hagen)
Description
Grid cells are neurons in the entorhinal cortex of rats and other mammals that
exhibit a very peculiar behavior: they fire at periodic locations that cover
the animal's environment in a regular, hexagonal lattice. Having a firing
behavior that is highly correlated with the animal's location grid cells might
provide a rare view on the general principles by which neurons in the
higher-order parts of the cortex process information. Based on this hypothesis
we developed a computational model of grid cells that postulates a dendritic
representation of each cell's input space that is learned by a self-organizing
process. We implemented this model and performed extensive simulations using the
ROOT data analysis framework. In this context, key features that are essential
for our work consist in the automatic serialization / deserialization of complex
data structures, efficient and fast storage and retrieval of serious amounts of
log data, fast and powerful visualization and analysis, as well as the fully
featured C++ interpreter.
Primary author
Jochen Kerdels
(University of Hagen)
