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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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Quality of 2-d place code, for increasing number of stored environments .(A–C) Rate maps of four example cells for 15, 30, and 90 stored remappings. The desired place field positions (not shown) are identical to Fig. 2 C, but in this case are hardly achieved. (D–F): Minimum mean squared error estimates of position plotted against true position for 500 trials, again for , 30 and 90. Parameters as in Fig. 2.
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pcbi-1003986-g007: Quality of 2-d place code, for increasing number of stored environments .(A–C) Rate maps of four example cells for 15, 30, and 90 stored remappings. The desired place field positions (not shown) are identical to Fig. 2 C, but in this case are hardly achieved. (D–F): Minimum mean squared error estimates of position plotted against true position for 500 trials, again for , 30 and 90. Parameters as in Fig. 2.

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)

Quality of 2-d place code, for increasing number of stored environments .(A–C) Rate maps of four example cells for 15, 30, and 90 stored remappings. The desired place field positions (not shown) are identical to Fig. 2 C, but in this case are hardly achieved. (D–F): Minimum mean squared error estimates of position plotted against true position for 500 trials, again for , 30 and 90. Parameters as in Fig. 2.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003986-g007: Quality of 2-d place code, for increasing number of stored environments .(A–C) Rate maps of four example cells for 15, 30, and 90 stored remappings. The desired place field positions (not shown) are identical to Fig. 2 C, but in this case are hardly achieved. (D–F): Minimum mean squared error estimates of position plotted against true position for 500 trials, again for , 30 and 90. Parameters as in Fig. 2.
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