Place and grid cell formation in the hippocampus of rodents has been studied intensely in the last decades, with the vast majority of these studies focusing on cognitive mapping in two dimensions. More recent approaches, however, are intrigued by extending this 2D paradigm to a fully volumetric understanding of place and grid cell design: Jeffrey et al. (2012) find experimental evidence that place and grid cells in rodents have strongly anisotropic characteristics, deducing that ground-dwelling animals seem to base their spatial navigation in three-dimensional environments on sets of 2D cognitive maps (s. Jeffrey et al., 2015). In experiments with flying bats, however, Yartsev et al. (2013) show that representation of space in place cells in these airborne animals is fully volumetric. To create a systematic understanding of place cell formation in three dimensions, this master project is geared towards extending work of Franzius et al. (2007), who propose a computational model for self-organization of place cells in the hippocampal formation of rodents. Their model applies Slow Feature Analysis (SFA) in order to learn two-dimensional place cell representations from image data, and has already been integrated into a virtual environment implemented in the Blender game engine. The focus of the project is on enhancing the existing 2D SFA model by enabling it to cope with up to 6 degrees of freedom. This augmented SFA model will then be used to reproduce and assess the various experimental results in 3D place cell formation within the virtual Blender environment. The simulation approach taken here will help to improve our theoretical understanding of cognitive mapping in volumetric environments, and will allow for effortless testing of multiple hypotheses concerning the true nature of cognitive mapping. An excellent command of Python (including numpy, matplotlib, scipy) and C/C++ is required. Experience with the Blender simulation environment would be advantageous.
Franzius, M., Sprekeler, H., & Wiskott, L. (2007). Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computational Biology, 3(8), e166.
Yartsev, M. M., Ulanovsky, N. (2013). Representation of Three-Dimensional Space in the Hippocampus of Flying Bats. Science, Vol. 340, Issue 6130, pp. 367-372.
Jeffery, K. J., Wilson, J. J., Casali, G., & Hayman, R. M. (2015). Neural encoding of large-scale three-dimensional space—properties and constraints. Frontiers in Psychology, 6, 927.
Dr.-Ing. Thomas Walther and Prof. Dr. Sen Cheng, Institute for Neural Computation