Hippocampal remapping and grid realignment in entorhinal cortex

By Marianne Fyhn, Torkel Hafting, Alessandro Treves, May-Britt Moser, Edvard I. Moser. Nature, 2007

760 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/b3618ed9e9f283ecf0309a2ff51c405a9f61a652

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Citations in the corpus

Place cells, grid cells, and the brain's spatial representation system. (2008, background): “The persistence of grid fields after removal or replacement of major landmarks points to self-motion information as the primary source for maintaining and updating grid representations (Hafting et al. 2005, Fyhn et al. 2007).”
Impaired spatial representation in CA1 after lesion of direct input from entorhinal cortex. (2008, background): “Converging evidence suggests that dynamic representations of place are computed in the entorhinal cortex and associated cortical areas (Fyhn et al., 2004, 2007; Hafting et al., 2005; Hargreaves et al., 2005; Sargolini et al., 2006) and that the hippocampus instead integrates the output from the…”
What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior. (2018, background): “Within a module, phase neighbors in one environment are phase neighbors in the other (Fyhn et al., 2007; Figure 3A).”
Human Replay Spontaneously Reorganizes Experience (2019, background): “Remapping experiments suggests that entorhinal cells encode structural knowledge explicitly—divorced from sensory representations—thereby enabling the same relational constraints to be applied to different sensory environments (Fyhn et al., 2007; Whittington et al., 2018; Yoon et al., 2013).”
Sequence Learning and the Role of the Hippocampus in Rodent Navigation (2012, neutral): “Both grid cells and head direction cells appear to be rather rigid and inflexible spatial representations, firing in similar patterns relative to each other across environments [7,8].”
The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation (2019, background): “These preserved properties provide the substrate for generalization of relational structure and are also observed in rodent grid cell populations recorded in multiple environments (Fyhn et al., 2007; Yoon et al., 2013).”
Attractor and integrator networks in the brain (2021, background): “6a) [166, 76], across the dimension of the spatial environment (6b) [167], and despite environmental rescaling that leads to large deformations in the spatial tuning of grid cells [76] (prediction 3)).”
From the neuron doctrine to neural networks (2015, methodology): “Attractor networks have also been used to model grid cells in the entorhinal cortex 125–127 and to explain their remapping in new environments 128 — something that is hard to understand from a single-neuron point of view.”
An intrinsic value system for developing multiple invariant representations with incremental slowness learning (2013, background): “…learns general structure from lots of data over a long period [7] — “It has been proposed that this universal spatial representation might be recoded onto a context-speciﬁc code in hippocampal networks, and that this interplay might be crucial for successful storage of episodic memories [12].””
Spatial Cognition and the Brain (2008, background): “The grids of neighboring grid cells are simply shifted copies of each other, so that the relative positions of the firing locations of two grid cells remain constant across an environment and also remain constant across different environments (Fyhn et al. 2007).”
An oscillatory interference model of grid cell firing (2007, background): “Interestingly, all of the grids within a given animal appear to have the same orientation (Fyhn et al., 2007; Barry et al., 2007).”
The neuroscience of learning: beyond the Hebbian synapse. (2013, background): “…place cells is that the entorhinal grid cell map (together with the head direction cells, border cells, and boundary-vector cells) appears to be a single map with different phase and alignment to the environment in different places or in different conditions (Fyhn et al. 2007, Solstad et al. 2008).”
Accurate Path Integration in Continuous Attractor Network Models of Grid Cells (2008, background): “Further, when the rat is placed in a novel environment, the SN grid responses are observed to isotropically expand or contract [32].”