Self-organization of spatially tuned neural activity in volumetric virtual environments Computational Neuroscience

Description

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.


Literature:

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.
http://doi.org/10.1371/journal.pcbi.0030166

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.
http://doi.org/10.1126/science.1235338

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.
http://doi.org/10.3389/fpsyg.2015.00927

Supervisors:
Dr.-Ing. Thomas Walther and Prof. Dr. Sen Cheng,  Institute for Neural Computation

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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