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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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Hebbian learning of multiple linear tracks.(A) Grid cell firing maps for 400 grid cells with width constant  on a 1 meter linear track (, ,  m, ). The cells are organized in 4 modules, with a period ratio of 1.67 to achieve a spatial period of 30 cm in the lowest module. The numbers at top right corners indicate the maximal spike count  as a proxy for peak firing rate (see Materials and Methods). (B) Firing rates of place cells which received the grid field activity from A as an input. The corresponding synaptic connections were obtained from an Hebbian outer product rule based on the rate maps of the grid population in A and the ideal place field population (C). (D) To represent a second environment, the grid code from A is shifted by module-specific phases. (E) Globally remapped place code that is learned from the remapped rate maps in D and F. (F) Ideal place code in the second environment.
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pcbi-1003986-g001: Hebbian learning of multiple linear tracks.(A) Grid cell firing maps for 400 grid cells with width constant on a 1 meter linear track (, , m, ). The cells are organized in 4 modules, with a period ratio of 1.67 to achieve a spatial period of 30 cm in the lowest module. The numbers at top right corners indicate the maximal spike count as a proxy for peak firing rate (see Materials and Methods). (B) Firing rates of place cells which received the grid field activity from A as an input. The corresponding synaptic connections were obtained from an Hebbian outer product rule based on the rate maps of the grid population in A and the ideal place field population (C). (D) To represent a second environment, the grid code from A is shifted by module-specific phases. (E) Globally remapped place code that is learned from the remapped rate maps in D and F. (F) Ideal place code in the second environment.

Mentions: Here we briefly summarize the general structure of our model, whereas a detailed account is provided in the Materials and Methods Section. A population of grid cells is connected to place cells via a feed-forward synaptic matrix. The grid cells are organized in four modules that differ in the spatial period (or grid spacing) of the periodic hexagonal firing patterns [17]. The neuronal activities of the MEC and hippocampal populations are assumed to encode either linear tracks or square boxes both of length 1 m (Figs. 1 and 2). Different environments are represented by phase shifts of the grid fields that are identical for all cells in a module [18] but random between modules [19].


Hippocampal remapping is constrained by sparseness rather than capacity.

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

Hebbian learning of multiple linear tracks.(A) Grid cell firing maps for 400 grid cells with width constant  on a 1 meter linear track (, ,  m, ). The cells are organized in 4 modules, with a period ratio of 1.67 to achieve a spatial period of 30 cm in the lowest module. The numbers at top right corners indicate the maximal spike count  as a proxy for peak firing rate (see Materials and Methods). (B) Firing rates of place cells which received the grid field activity from A as an input. The corresponding synaptic connections were obtained from an Hebbian outer product rule based on the rate maps of the grid population in A and the ideal place field population (C). (D) To represent a second environment, the grid code from A is shifted by module-specific phases. (E) Globally remapped place code that is learned from the remapped rate maps in D and F. (F) Ideal place code in the second environment.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003986-g001: Hebbian learning of multiple linear tracks.(A) Grid cell firing maps for 400 grid cells with width constant on a 1 meter linear track (, , m, ). The cells are organized in 4 modules, with a period ratio of 1.67 to achieve a spatial period of 30 cm in the lowest module. The numbers at top right corners indicate the maximal spike count as a proxy for peak firing rate (see Materials and Methods). (B) Firing rates of place cells which received the grid field activity from A as an input. The corresponding synaptic connections were obtained from an Hebbian outer product rule based on the rate maps of the grid population in A and the ideal place field population (C). (D) To represent a second environment, the grid code from A is shifted by module-specific phases. (E) Globally remapped place code that is learned from the remapped rate maps in D and F. (F) Ideal place code in the second environment.
Mentions: Here we briefly summarize the general structure of our model, whereas a detailed account is provided in the Materials and Methods Section. A population of grid cells is connected to place cells via a feed-forward synaptic matrix. The grid cells are organized in four modules that differ in the spatial period (or grid spacing) of the periodic hexagonal firing patterns [17]. The neuronal activities of the MEC and hippocampal populations are assumed to encode either linear tracks or square boxes both of length 1 m (Figs. 1 and 2). Different environments are represented by phase shifts of the grid fields that are identical for all cells in a module [18] but random between modules [19].

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