Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Chaudhuri-Vayalambrone, Prannoy; Rule, Michael E; Bauža, Marius; Krstulovic, Marino; Kerekes, Pauline; Burton, Stephen; O’Leary, Timothy; Krupic, Julija

doi:10.1016/j.celrep.2023.112716

Cited by 6 publications

(2 citation statements)

References 70 publications

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“…Furthermore, grid cell sequences have been shown to be useful in simulations of new goal-directed trajectories, where grid cells were used in a network of head direction cells, hippocampal place cells and persistent spiking cells to plan forward trajectories through the environment that search for place cells near a goal location 16 . Recent experimental data demonstrate that temporally coordinated entorhinal inputs drive hippocampal sequences to perform memory-guided navigation 28 and that temporally structured population activity of grid and place cells represent local trajectories essential for goal-directed navigation and planning 64 .…”

Section: Discussionmentioning

confidence: 99%

Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories

Rebecca¹,

Ascoli

Dannenberg

2023

Preprint

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Spatial periodicity in grid cell firing has been interpreted as a neural metric for space providing animals with a coordinate system in navigating physical and mental spaces. However, the specific computational problem being solved by grid cells has remained elusive. We here provide mathematical proof that spatial periodicity in grid cell firing is the only possible solution to a neural sequence code of 2D trajectories and that the hexagonal firing pattern of grid cells is the most parsimonious solution to such a sequence code. We thereby provide a teleological cause for the existence of grid cells and reveal the underlying nature of the global geometrical organization in grid maps as a direct consequence of a simple local sequence code using a minimal number of neurons. A sequence code by grid cells provides intuitive explanations for many previously puzzling experimental observations and may transform our thinking about grid cells.

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Section: Discussionmentioning

confidence: 99%

Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories

Rebecca¹,

Ascoli

Dannenberg

2023

Preprint

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“…Throughout this text, we will demonstrate GP methods on data from Krupic et al (2018), which have also been presented in Chaudhuri-Vayalambrone et al (2023). Figure 1 illustrates a spatial-navigation experiment (Krupic et al, 2018) in which a rat foraged in a 2 m Â 1 m environment (Figure 1a).…”

Section: An Example Experimentsmentioning

confidence: 99%

Variational log‐Gaussian point‐process methods for grid cells

Rule,

Chaudhuri‐Vayalambrone,

Krstulovic

et al. 2023

Hippocampus

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We present practical solutions to applying Gaussian‐process (GP) methods to calculate spatial statistics for grid cells in large environments. GPs are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log‐Gaussian Poisson models can be calculated quickly. This class of models has closed‐form expressions for the evidence lower‐bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low‐rank spatial frequency subspace for further acceleration, and demonstrate these methods on experimental data.

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Variational Log-Gaussian Point-Process Methods for Grid Cells

Rule

Vayalambrone

Krstulovic

et al. 2023

Preprint

View full text Add to dashboard Cite

We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. Krylov-subspace algorithms in a low-rank spatial frequency subspace provide further acceleration. We demonstrate these methods on a recording from grid cells in a large environment.

show abstract

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Cited by 6 publications

References 70 publications

Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories

Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories

Variational log‐Gaussian point‐process methods for grid cells

Variational Log-Gaussian Point-Process Methods for Grid Cells

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