The article investigates how grid cells can reduce spatial aliasing in hippocampal place representations that arise when boundary vector cell (BVC) inputs alone produce indistinguishable sensory patterns across multiple locations. By integrating analytically constructed grid cell modules with BVC-driven place cells, the study demonstrates a 94–99% reduction in spatial aliasing across three environments, with the greatest improvement in the most visually symmetric maze. The results show that grid cells provide complementary spatial signals that disambiguate perceptually identical locations.
arXiv:2406. 07049v3 Announce Type: replace-cross Abstract: Understanding spatial relationships across all dimensions is fundamental for intelligent systems.
arXiv:2603.15412v2 Announce Type: replace Abstract: The mammalian brain, most extensively studied in rodents and bats, solves an enormous variety of non-spatial cognitive tasks using neural circuitry...
arXiv:2609.13219v1 Announce Type: cross Abstract: Neural correlates of spatial cognitive map are well documented, yet exactly how neural circuits perform spatial navigation in complex environments -...
arXiv:2607. 12382v1 Announce Type: new Abstract: How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact sensory patterns rarely repeat?
How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact sensory patterns rarely repeat? The Clone-Structured Causal Graph algorithm (CSCG), a normative hippocampus model, shows how an interpretable map can be learned from aliased observations.
arXiv:2606. 00124v1 Announce Type: cross Abstract: Positional embeddings (PEs) in Vision Transformers (ViTs) are known to impact performance and robustness, but their role in shaping internal spatial representations is not well understood.
arXiv:2606. 27831v1 Announce Type: cross Abstract: This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framework based on biological hippocampal memory modeling.
arXiv:2609.39238v1 Announce Type: new Abstract: An agent that moves must recognise a place from a viewpoint it has never seen. We introduce 4MT-VLM, a dataset of procedurally generated landscapes, ea...
arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences....
The paper investigates how data scaling should be viewed as an allocation problem rather than a simple sample count, focusing on spatially structured data from human brain histology. By separating unique sample count, source diversity, and spatial coverage, the authors conduct 93 pretraining runs on 11.6 million image patches from 21 brains, showing that performance improves with more unique samples, broader spatial coverage, more compute, and larger models. However, at a fixed sample budget, distributing samples across multiple subjects does not yield additional benefit, indicating that inter‑subject variation impacts generalization but adding more sources does not help when the sample count is held constant.
Location encoders transform geographic coordinates into high‑dimensional embeddings for machine learning, yet it is unclear how well these embeddings capture interpretable spatial effects. This study benchmarks GeoShapley—a game‑theoretic explainer treating all location features as a single joint player—against eleven TorchSpatial encoders on a synthetic process with known coefficients, across grid, county, and global scales, with and without raw coordinates and under different training regimes. The results show that primary coefficient recovery is consistently high across encoders, while secondary coefficient recovery varies more with scale, especially at the global level, and raw‑coordinate baselines remain competitive throughout.
arXiv:2608. 06659v1 Announce Type: new Abstract: This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics.