Microstructure of a spatial map in the entorhinal cortex

By Torkel Hafting, Marianne Fyhn, S. Molden, May-Britt Moser, Edvard I. Moser. Nature, 2005

3,942 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b0004097eac896ee4bf8b45ba3a7d9f101782542

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

The Routledge Handbook of Philosophy of Animal Minds (2017, background): “• The rat entorhinal cortex contains grid cells (Hafting et al. 2005), each responding selectively to multiple spatial locations in the available environment.”
The Role of Hippocampal Replay in Memory and Planning (2018, background): “world: head direction cells, found throughout the limbic system, signal direction of facing [36–38]; grid cells in the medial entorhinal cortex and para-subiculum represent self-location with an efficient periodic code [39,40]; and border cells aswell as bound-”
Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams (2012, background): “In rats, grid cells are found in entorhinal cortex (EC) (Hafting et al., 2005), which feeds into the hippocampus.”
Viewpoints: how the hippocampus contributes to memory, navigation and cognition (2017, background): “Early work (Hafting et al., 2005) showed that grid cell firing patterns persist during open-field foraging in the dark, suggesting that somewhere in the brain, information generated by self-motion is encoded.”
Using Grid Cells for Navigation (2015, neutral): “…grid cells are widely believed to provide a path integration input to place cells, updating the representation of self-location by a vector describing the animal’s recent motion (Hafting et al., 2005; O’Keefe and Burgess, 2005; McNaughton et al., 2006; Rolls et al., 2006; Solstad et al., 2006).”
An oscillatory interference model of grid cell firing (2007, background): “Grids do not change in size when the animal is moved between two different familiar boxes of different size (Hafting et al., 2005).”
The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation (2019, background): “Following Eichenbaum (14), hippocampal representations, (p), are inferred as a conjunction between sensory input (x) in the lateral (LEC), and abstract location (g) in the medial (MEC) entorhinal cortex.”
Sequence Learning and the Role of the Hippocampus in Rodent Navigation (2012, neutral): “Cells of one class, called grid cells, fire in a geometrically regular pattern of locations that form a pattern of equilateral triangles that tessellate the floor of an environment [4 ,5].”
Elements of a neurobiological theory of hippocampal function: the role of synaptic plasticity, synaptic tagging and schemas (2006, background): “Journal Compilation ª Federation of European Neuroscience Societies and Blackwell Publishing Ltd European Journal of Neuroscience, 23, 2829–2846 (Quirk et al., 1992; Frank et al., 2000; Fyhn et al., 2004; Hafting et al., 2005).”
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems (2016, background): “Despite the computational difﬁculties, animals exhibit stable neural tuning in familiar and novel environments over several 10s of minutes [1, 2], even though the PI estimates in the same animals is estimated to deteriorate within a few minutes [3].”
Predictive Processing: A Canonical Cortical Computation (2018, neutral): “This concept has been a guiding principle for scientific inquiry; it is apparent not only in the concept of receptive fields of neurons in visual cortex, but also in place cells (O’Keefe and Dostrovsky, 1971), grid cells (Hafting et al., 2005), face cells (Perrett et al.”