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Hippocampal remapping is constrained by sparseness rather than capacity.

Kammerer A, Leibold C - PLoS Comput. Biol. (2014)

Bottom Line: We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code.Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information.Both populations may encode different aspects of space.

View Article: PubMed Central - PubMed

Affiliation: Department Biologie II, Ludwig-Maximilians-Universität München, Planegg, Germany; Graduate School for Systemic Neurosciences, Ludwig-Maximilians-Universität München, Planegg, Germany.

ABSTRACT
Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space.

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Capacity for storing remappings in a square box.Place cell resolution and further measures as functions of the number  of remappings stored. (A) Root mean square error (RMSE) of place cells. Blue and green solid lines: Mean over realizations. Dashed lines: 99 quantiles. Red line RMSE of the grid cell input. (B) Mean single cell sparseness. (C) Ratio of proper place cells. (D) Mean number of place fields for the proper place cells. (E) Mean size of place fields for the proper place cells. (F) Mean population sparseness. (G) Ratio of cells for which Hebbian learning of place fields was successful (according to the three similarity criteria defined in the Materials and Methods section). Parameter used as before ,  m, , , , 4 modules, 15 realizations.
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pcbi-1003986-g008: Capacity for storing remappings in a square box.Place cell resolution and further measures as functions of the number of remappings stored. (A) Root mean square error (RMSE) of place cells. Blue and green solid lines: Mean over realizations. Dashed lines: 99 quantiles. Red line RMSE of the grid cell input. (B) Mean single cell sparseness. (C) Ratio of proper place cells. (D) Mean number of place fields for the proper place cells. (E) Mean size of place fields for the proper place cells. (F) Mean population sparseness. (G) Ratio of cells for which Hebbian learning of place fields was successful (according to the three similarity criteria defined in the Materials and Methods section). Parameter used as before , m, , , , 4 modules, 15 realizations.

Mentions: To test whether these observations are specific to the one-dimensional paradigm, we repeated the same simulations and analysis for a two-dimensional enclosure (see Materials and Methods and Fig. 2). As in the one-dimensional case, inspection of single examples for high numbers of remappings reveals that the place-selectivity of the readout neurons (the putative place cells) deteriorates much faster than the decoding quality (Fig. 7). Even random spatial patches (for ; Fig. 7 B) allow for almost perfect decoding (Fig. 7 E). Spatial estimation only breaks down, if hardly any space modulation is observable in the firing patterns (Fig. 7 C, F). These exemplary observations are corroborated by a systematic quantitative assessment of the code and the firing fields in Fig. 8.


Hippocampal remapping is constrained by sparseness rather than capacity.

Kammerer A, Leibold C - PLoS Comput. Biol. (2014)

Capacity for storing remappings in a square box.Place cell resolution and further measures as functions of the number  of remappings stored. (A) Root mean square error (RMSE) of place cells. Blue and green solid lines: Mean over realizations. Dashed lines: 99 quantiles. Red line RMSE of the grid cell input. (B) Mean single cell sparseness. (C) Ratio of proper place cells. (D) Mean number of place fields for the proper place cells. (E) Mean size of place fields for the proper place cells. (F) Mean population sparseness. (G) Ratio of cells for which Hebbian learning of place fields was successful (according to the three similarity criteria defined in the Materials and Methods section). Parameter used as before ,  m, , , , 4 modules, 15 realizations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256019&req=5

pcbi-1003986-g008: Capacity for storing remappings in a square box.Place cell resolution and further measures as functions of the number of remappings stored. (A) Root mean square error (RMSE) of place cells. Blue and green solid lines: Mean over realizations. Dashed lines: 99 quantiles. Red line RMSE of the grid cell input. (B) Mean single cell sparseness. (C) Ratio of proper place cells. (D) Mean number of place fields for the proper place cells. (E) Mean size of place fields for the proper place cells. (F) Mean population sparseness. (G) Ratio of cells for which Hebbian learning of place fields was successful (according to the three similarity criteria defined in the Materials and Methods section). Parameter used as before , m, , , , 4 modules, 15 realizations.
Mentions: To test whether these observations are specific to the one-dimensional paradigm, we repeated the same simulations and analysis for a two-dimensional enclosure (see Materials and Methods and Fig. 2). As in the one-dimensional case, inspection of single examples for high numbers of remappings reveals that the place-selectivity of the readout neurons (the putative place cells) deteriorates much faster than the decoding quality (Fig. 7). Even random spatial patches (for ; Fig. 7 B) allow for almost perfect decoding (Fig. 7 E). Spatial estimation only breaks down, if hardly any space modulation is observable in the firing patterns (Fig. 7 C, F). These exemplary observations are corroborated by a systematic quantitative assessment of the code and the firing fields in Fig. 8.

Bottom Line: We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code.Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information.Both populations may encode different aspects of space.

View Article: PubMed Central - PubMed

Affiliation: Department Biologie II, Ludwig-Maximilians-Universität München, Planegg, Germany; Graduate School for Systemic Neurosciences, Ludwig-Maximilians-Universität München, Planegg, Germany.

ABSTRACT
Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space.

Show MeSH
Related in: MedlinePlus