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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 place cells RMSEplace on linear track.(A–F) Place cell tuning functions (spike counts  as a function of space ). Dashed lines: teacher tuning curves used for training. Solid lines: tuning curves after learning averaged over 800 trials ( is the width of the teacher curves). (G) Place cell resolution RMSEplace as function of  and . Grid cell resolution is shown as dashed line. Parameters used were , , , other parameters were as in Fig. 3.
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pcbi-1003986-g004: Root mean square error of place cells RMSEplace on linear track.(A–F) Place cell tuning functions (spike counts as a function of space ). Dashed lines: teacher tuning curves used for training. Solid lines: tuning curves after learning averaged over 800 trials ( is the width of the teacher curves). (G) Place cell resolution RMSEplace as function of and . Grid cell resolution is shown as dashed line. Parameters used were , , , other parameters were as in Fig. 3.

Mentions: The spatial acuity of the population code of grid cells can only be made use of if it can be read out by downstream centers. We therefore asked under which conditions the resolution of grid cell network from the previous subsection can be preserved in the place cell network under the ideal conditions that only one environment has to be represented (number of environments ); Fig. 4.


Hippocampal remapping is constrained by sparseness rather than capacity.

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

Root mean square error of place cells RMSEplace on linear track.(A–F) Place cell tuning functions (spike counts  as a function of space ). Dashed lines: teacher tuning curves used for training. Solid lines: tuning curves after learning averaged over 800 trials ( is the width of the teacher curves). (G) Place cell resolution RMSEplace as function of  and . Grid cell resolution is shown as dashed line. Parameters used were , , , other parameters were as in Fig. 3.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003986-g004: Root mean square error of place cells RMSEplace on linear track.(A–F) Place cell tuning functions (spike counts as a function of space ). Dashed lines: teacher tuning curves used for training. Solid lines: tuning curves after learning averaged over 800 trials ( is the width of the teacher curves). (G) Place cell resolution RMSEplace as function of and . Grid cell resolution is shown as dashed line. Parameters used were , , , other parameters were as in Fig. 3.
Mentions: The spatial acuity of the population code of grid cells can only be made use of if it can be read out by downstream centers. We therefore asked under which conditions the resolution of grid cell network from the previous subsection can be preserved in the place cell network under the ideal conditions that only one environment has to be represented (number of environments ); Fig. 4.

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