Optimal Population Codes for Space: Grid Cells Outperform Place Cells.
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| Title: | Optimal Population Codes for Space: Grid Cells Outperform Place Cells. |
|---|---|
| Authors: | Mathis, Alexander, Herz, Andreas V. M., Stemmler, Martin |
| Source: | Neural Computation. Sep2012, Vol. 24 Issue 9, p2280-2317. 38p. 2 Diagrams, 7 Graphs. |
| Subjects: | Hippocampus (Brain), Entorhinal cortex, A priori, Maximum likelihood statistics, Cerebral cortex, Limbic system |
| 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 resolutionwhen 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, usingmaximum 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 78349348 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal Population Codes for Space: Grid Cells Outperform Place Cells. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mathis%2C+Alexander%22">Mathis, Alexander</searchLink><br /><searchLink fieldCode="AR" term="%22Herz%2C+Andreas+V%2E+M%2E%22">Herz, Andreas V. M.</searchLink><br /><searchLink fieldCode="AR" term="%22Stemmler%2C+Martin%22">Stemmler, Martin</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Sep2012, Vol. 24 Issue 9, p2280-2317. 38p. 2 Diagrams, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hippocampus+%28Brain%29%22">Hippocampus (Brain)</searchLink><br /><searchLink fieldCode="DE" term="%22Entorhinal+cortex%22">Entorhinal cortex</searchLink><br /><searchLink fieldCode="DE" term="%22A+priori%22">A priori</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Cerebral+cortex%22">Cerebral cortex</searchLink><br /><searchLink fieldCode="DE" term="%22Limbic+system%22">Limbic system</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 resolutionwhen 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, usingmaximum 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/NECO_a_00319 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 2280 Subjects: – SubjectFull: Hippocampus (Brain) Type: general – SubjectFull: Entorhinal cortex Type: general – SubjectFull: A priori Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Cerebral cortex Type: general – SubjectFull: Limbic system Type: general Titles: – TitleFull: Optimal Population Codes for Space: Grid Cells Outperform Place Cells. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mathis, Alexander – PersonEntity: Name: NameFull: Herz, Andreas V. M. – PersonEntity: Name: NameFull: Stemmler, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 24 – Type: issue Value: 9 Titles: – TitleFull: Neural Computation Type: main |
| ResultId | 1 |
