Literature DB >> 23576417

Spatial scale and place field stability in a grid-to-place cell model of the dorsoventral axis of the hippocampus.

David Lyttle1, Brian Gereke, Kevin K Lin, Jean-Marc Fellous.   

Abstract

The rodent hippocampus and entorhinal cortex contain spatially modulated cells that serve as the basis for spatial coding. Both medial entorhinal grid cells and hippocampal place cells have been shown to encode spatial information across multiple spatial scales that increase along the dorsoventral axis of these structures. Place cells near the dorsal pole possess small, stable, and spatially selective firing fields, while ventral cells have larger, less stable, and less spatially selective firing fields. One possible explanation for these dorsoventral changes in place field properties is that they arise as a result of similar dorsoventral differences in the properties of the grid cell inputs to place cells. Here, we test the alternative hypothesis that dorsoventral place field differences are due to higher amounts of nonspatial inputs to ventral hippocampal cells. We use a computational model of the entorhinal-hippocampal network to assess the relative contributions of grid scale and nonspatial inputs in determining place field size and stability. In addition, we assess the consequences of grid node firing rate heterogeneity on place field stability. Our results suggest that dorsoventral differences in place cell properties can be better explained by changes in the amount of nonspatial inputs, rather than by changes in the scale of grid cell inputs, and that grid node heterogeneity may have important functional consequences. The observed gradient in field size may reflect a shift from processing primarily spatial information in the dorsal hippocampus to processing more nonspatial, contextual, and emotional information near the ventral hippocampus.
Copyright © 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  computational model; dorsoventral; grid cells; hippocampus; place cells

Mesh:

Year:  2013        PMID: 23576417      PMCID: PMC4120775          DOI: 10.1002/hipo.22132

Source DB:  PubMed          Journal:  Hippocampus        ISSN: 1050-9631            Impact factor:   3.899


  44 in total

1.  Grid cells use HCN1 channels for spatial scaling.

Authors:  Lisa M Giocomo; Syed A Hussaini; Fan Zheng; Eric R Kandel; May-Britt Moser; Edvard I Moser
Journal:  Cell       Date:  2011-11-17       Impact factor: 41.582

2.  Grid cells in rat entorhinal cortex encode physical space with independent firing fields and phase precession at the single-trial level.

Authors:  Eric T Reifenstein; Richard Kempter; Susanne Schreiber; Martin B Stemmler; Andreas V M Herz
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-02       Impact factor: 11.205

Review 3.  Functional organization of the extrinsic and intrinsic circuitry of the parahippocampal region.

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Journal:  Prog Neurobiol       Date:  1989       Impact factor: 11.685

4.  A spin glass model of path integration in rat medial entorhinal cortex.

Authors:  Mark C Fuhs; David S Touretzky
Journal:  J Neurosci       Date:  2006-04-19       Impact factor: 6.167

5.  How heterogeneous place cell responding arises from homogeneous grids--a contextual gating hypothesis.

Authors:  Robin M Hayman; Kathryn J Jeffery
Journal:  Hippocampus       Date:  2008       Impact factor: 3.899

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Authors:  J O'Keefe; N Burgess
Journal:  Nature       Date:  1996-05-30       Impact factor: 49.962

Review 7.  The dopaminergic mesencephalic projections to the hippocampal formation in the rat.

Authors:  A Gasbarri; A Sulli; M G Packard
Journal:  Prog Neuropsychopharmacol Biol Psychiatry       Date:  1997-01       Impact factor: 5.067

8.  The entorhinal grid map is discretized.

Authors:  Hanne Stensola; Tor Stensola; Trygve Solstad; Kristian Frøland; May-Britt Moser; Edvard I Moser
Journal:  Nature       Date:  2012-12-06       Impact factor: 49.962

9.  Spatial memory in the rat requires the dorsolateral band of the entorhinal cortex.

Authors:  Hill-Aina Steffenach; Menno Witter; May-Britt Moser; Edvard I Moser
Journal:  Neuron       Date:  2005-01-20       Impact factor: 17.173

10.  Distinct representations and theta dynamics in dorsal and ventral hippocampus.

Authors:  Sébastien Royer; Anton Sirota; Jagdish Patel; György Buzsáki
Journal:  J Neurosci       Date:  2010-02-03       Impact factor: 6.167

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  11 in total

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Journal:  J Neurosci       Date:  2017-03-09       Impact factor: 6.167

2.  Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network.

Authors:  Kathryn R Hedrick; Kechen Zhang
Journal:  J Neurophysiol       Date:  2016-05-18       Impact factor: 2.714

Review 3.  Dynamical self-organization and efficient representation of space by grid cells.

Authors:  Ronald W DiTullio; Vijay Balasubramanian
Journal:  Curr Opin Neurobiol       Date:  2021-11-30       Impact factor: 6.627

4.  Grid scale drives the scale and long-term stability of place maps.

Authors:  Caitlin S Mallory; Kiah Hardcastle; Jason S Bant; Lisa M Giocomo
Journal:  Nat Neurosci       Date:  2018-01-15       Impact factor: 24.884

Review 5.  What do grid cells contribute to place cell firing?

Authors:  Daniel Bush; Caswell Barry; Neil Burgess
Journal:  Trends Neurosci       Date:  2014-01-30       Impact factor: 13.837

6.  Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model.

Authors:  Justin Lines; Kelsey Nation; Jean-Marc Fellous
Journal:  Comput Intell Neurosci       Date:  2017-07-03

7.  From grid cells to place cells with realistic field sizes.

Authors:  Torsten Neher; Amir Hossein Azizi; Sen Cheng
Journal:  PLoS One       Date:  2017-07-27       Impact factor: 3.240

8.  Differences in reward biased spatial representations in the lateral septum and hippocampus.

Authors:  Hannah S Wirtshafter; Matthew A Wilson
Journal:  Elife       Date:  2020-05-26       Impact factor: 8.140

9.  Spatiotemporally random and diverse grid cell spike patterns contribute to the transformation of grid cell to place cell in a neural network model.

Authors:  Sahn Woo Park; Hyun Jae Jang; Mincheol Kim; Jeehyun Kwag
Journal:  PLoS One       Date:  2019-11-14       Impact factor: 3.240

10.  Reward anticipation selectively boosts encoding of gist for visual objects.

Authors:  Liyana T Swirsky; Ryan M Marinacci; Julia Spaniol
Journal:  Sci Rep       Date:  2020-11-19       Impact factor: 4.379

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