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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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Effect of the E% parameter on the capacity for storing remappings in a square box for  place cells.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, 8 realizations. The curve for  is taken from Figure 8 and has 15 realizations.
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pcbi-1003986-g009: Effect of the E% parameter on the capacity for storing remappings in a square box for place cells.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, 8 realizations. The curve for is taken from Figure 8 and has 15 realizations.

Mentions: A substantial effect on the population code can be observed by altering the strength of feedback inhibition in the place field population by means of the E% value (Fig. 9). This parameter determines the firing threshold as the input strength E% below the maximum (see Methods and [21]). The E% value directly controls the sparseness of the code (Fig. 9B–G). For low E% values (sparse codes) and low numbers of environments, we again observe the finite size effect of high RMSE, which then improves with increasing (Fig. 9A). This initially high RMSE, however, can again be compensated for by using larger numbers of place cells (as in Fig. 8 A). As a result, the decreasing E% generally allows to store more environments, however, at the cost of high to achieve a sufficiently small RMSE for low .


Hippocampal remapping is constrained by sparseness rather than capacity.

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

Effect of the E% parameter on the capacity for storing remappings in a square box for  place cells.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, 8 realizations. The curve for  is taken from Figure 8 and has 15 realizations.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003986-g009: Effect of the E% parameter on the capacity for storing remappings in a square box for place cells.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, 8 realizations. The curve for is taken from Figure 8 and has 15 realizations.
Mentions: A substantial effect on the population code can be observed by altering the strength of feedback inhibition in the place field population by means of the E% value (Fig. 9). This parameter determines the firing threshold as the input strength E% below the maximum (see Methods and [21]). The E% value directly controls the sparseness of the code (Fig. 9B–G). For low E% values (sparse codes) and low numbers of environments, we again observe the finite size effect of high RMSE, which then improves with increasing (Fig. 9A). This initially high RMSE, however, can again be compensated for by using larger numbers of place cells (as in Fig. 8 A). As a result, the decreasing E% generally allows to store more environments, however, at the cost of high to achieve a sufficiently small RMSE for low .

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