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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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Root mean square error of grid cells (RMSEgrid) on the linear track.(A–C) Example tuning curves for 4 cells from different modules and three choices of width constant . (D) RMSE as a function of cell number  and tuning width . (E) Scaling of  with  for fixed RMSE. Parameters are ,  m, .
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pcbi-1003986-g003: Root mean square error of grid cells (RMSEgrid) on the linear track.(A–C) Example tuning curves for 4 cells from different modules and three choices of width constant . (D) RMSE as a function of cell number and tuning width . (E) Scaling of with for fixed RMSE. Parameters are , m, .

Mentions: To assess the ground truth of our model, we first evaluate the coding capacity of the grid cell population on a one-dimensional linear track (Fig. 3). The spatial resolution (denoted as root-mean square estimation error; RMSE) non-trivially depends on the tuning width of the grid code and the number of neurons [9], [22]. Three examples of grid codes are shown in Fig. 3A–C for three different values of . Grids as usually observed in MEC are most similar to the situation in Fig. 3B, whereas Fig. 3A and C illustrate settings with extremely thin and broad tuning curves, respectively. Thus, the biological value of is about 1, which corresponds to a ratio between tuning width and spatial period of about (see Fig. S4 of [3]). However, the RMSE non-monotonically depends on [22] with a minimum at rather thin tuning curves (Fig. 3D).


Hippocampal remapping is constrained by sparseness rather than capacity.

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

Root mean square error of grid cells (RMSEgrid) on the linear track.(A–C) Example tuning curves for 4 cells from different modules and three choices of width constant . (D) RMSE as a function of cell number  and tuning width . (E) Scaling of  with  for fixed RMSE. Parameters are ,  m, .
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003986-g003: Root mean square error of grid cells (RMSEgrid) on the linear track.(A–C) Example tuning curves for 4 cells from different modules and three choices of width constant . (D) RMSE as a function of cell number and tuning width . (E) Scaling of with for fixed RMSE. Parameters are , m, .
Mentions: To assess the ground truth of our model, we first evaluate the coding capacity of the grid cell population on a one-dimensional linear track (Fig. 3). The spatial resolution (denoted as root-mean square estimation error; RMSE) non-trivially depends on the tuning width of the grid code and the number of neurons [9], [22]. Three examples of grid codes are shown in Fig. 3A–C for three different values of . Grids as usually observed in MEC are most similar to the situation in Fig. 3B, whereas Fig. 3A and C illustrate settings with extremely thin and broad tuning curves, respectively. Thus, the biological value of is about 1, which corresponds to a ratio between tuning width and spatial period of about (see Fig. S4 of [3]). However, the RMSE non-monotonically depends on [22] with a minimum at rather thin tuning curves (Fig. 3D).

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