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This research demonstrates how integrating grid cell signals with boundary vector cells significantly reduces spatial aliasing in neural place representations, particularly within geometrically ambiguous environments. By leveraging internally generated periodic patterns independent of environmental geometry, the study achieves a 94–99% reduction in representation confusion compared to models relying solely on sensory inputs. These findings advance our understanding of biological navigation mechanisms and offer robust architectural principles for developing more reliable AI agents operating in repetitive or symmetric physical spaces.
The Role of Grid Cells in Reducing Spatial Aliasing in Hippocampal Place Representations
arXiv:2608.18569v1 Announce Type: cross Abstract: Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94--99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.
Abstract:Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94--99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY); Neurons and Cognition (q-bio.NC)
arXiv:2608.18569 [cs.NE]
(or arXiv:2608.18569v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2608.18569 arXiv-issued DOI via DataCite (pending registration)
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