Literature DB >> 22594833

Optimal population codes for space: grid cells outperform place cells.

Alexander Mathis1, Andreas V M Herz, Martin Stemmler.   

Abstract

Rodents use two distinct neuronal coordinate systems to estimate their position: place fields in the hippocampus and grid fields in the entorhinal cortex. Whereas place cells spike at only one particular spatial location, grid cells fire at multiple sites that correspond to the points of an imaginary hexagonal lattice. We study how to best construct place and grid codes, taking the probabilistic nature of neural spiking into account. Which spatial encoding properties of individual neurons confer the highest resolution when decoding the animal's position from the neuronal population response? A priori, estimating a spatial position from a grid code could be ambiguous, as regular periodic lattices possess translational symmetry. The solution to this problem requires lattices for grid cells with different spacings; the spatial resolution crucially depends on choosing the right ratios of these spacings across the population. We compute the expected error in estimating the position in both the asymptotic limit, using Fisher information, and for low spike counts, using maximum likelihood estimation. Achieving high spatial resolution and covering a large range of space in a grid code leads to a trade-off: the best grid code for spatial resolution is built of nested modules with different spatial periods, one inside the other, whereas maximizing the spatial range requires distinct spatial periods that are pairwisely incommensurate. Optimizing the spatial resolution predicts two grid cell properties that have been experimentally observed. First, short lattice spacings should outnumber long lattice spacings. Second, the grid code should be self-similar across different lattice spacings, so that the grid field always covers a fixed fraction of the lattice period. If these conditions are satisfied and the spatial "tuning curves" for each neuron span the same range of firing rates, then the resolution of the grid code easily exceeds that of the best possible place code with the same number of neurons.

Mesh:

Year:  2012        PMID: 22594833     DOI: 10.1162/NECO_a_00319

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  41 in total

1.  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 2.  Spatial representation in the hippocampal formation: a history.

Authors:  Edvard I Moser; May-Britt Moser; Bruce L McNaughton
Journal:  Nat Neurosci       Date:  2017-10-26       Impact factor: 24.884

3.  Deforming the metric of cognitive maps distorts memory.

Authors:  Jacob L S Bellmund; William de Cothi; Tom A Ruiter; Matthias Nau; Caswell Barry; Christian F Doeller
Journal:  Nat Hum Behav       Date:  2019-11-18

4.  Anticipatory Neural Activity Improves the Decoding Accuracy for Dynamic Head-Direction Signals.

Authors:  Johannes Zirkelbach; Martin Stemmler; Andreas V M Herz
Journal:  J Neurosci       Date:  2019-01-28       Impact factor: 6.167

5.  Neurons and networks organizing and sequencing memories.

Authors:  Sam A Deadwyler; Theodore W Berger; Ioan Opris; Dong Song; Robert E Hampson
Journal:  Brain Res       Date:  2014-12-29       Impact factor: 3.252

6.  A principle of economy predicts the functional architecture of grid cells.

Authors:  Xue-Xin Wei; Jason Prentice; Vijay Balasubramanian
Journal:  Elife       Date:  2015-09-03       Impact factor: 8.140

7.  Neural representation of spatial topology in the rodent hippocampus.

Authors:  Zhe Chen; Stephen N Gomperts; Jun Yamamoto; Matthew A Wilson
Journal:  Neural Comput       Date:  2013-10-08       Impact factor: 2.026

Review 8.  Grid cells and cortical representation.

Authors:  Edvard I Moser; Yasser Roudi; Menno P Witter; Clifford Kentros; Tobias Bonhoeffer; May-Britt Moser
Journal:  Nat Rev Neurosci       Date:  2014-06-11       Impact factor: 34.870

9.  Remapping and realignment in the human hippocampal formation predict context-dependent spatial behavior.

Authors:  Joshua B Julian; Christian F Doeller
Journal:  Nat Neurosci       Date:  2021-04-15       Impact factor: 24.884

10.  Place-cell capacity and volatility with grid-like inputs.

Authors:  Man Yi Yim; Lorenzo A Sadun; Ila R Fiete; Thibaud Taillefumier
Journal:  Elife       Date:  2021-05-24       Impact factor: 8.140

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