Medial Entorhinal Grid Cells and Head Direction Cells Rotate with a T-Maze More Often During Less Recently Experienced Rotations

Gupta, Kishan; Beer, Nathan J.; Keller, Lauren A.; Hasselmo, Michael E.

doi:10.1093/cercor/bht020

Cited by 15 publications

(15 citation statements)

References 57 publications

(116 reference statements)

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“…Similarly, enlarging or shrinking the size of the environment by moving barriers causes a compression or expansion of the firing fields of place cells (O'Keefe and Burgess, 1996) and grid cells (Barry et al, 2007). Adding barriers to the environment that require the animal to move in constrained trajectories causes grid cells and place cells to respond more strongly to the one dimensional position of the animal along a trajectory (Derdikman et al, 2009; Gupta et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Head direction is coded more strongly than movement direction in a population of entorhinal neurons

et al. 2015

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The spatial firing pattern of entorhinal grid cells may be important for navigation. Many different computational models of grid cell firing use path integration based on movement direction and the associated movement speed to drive grid cells. However, the response of neurons to movement direction has rarely been tested, in contrast to multiple studies showing responses of neurons to head direction. Here, we analyzed the difference between head direction and movement direction during rat movement and analyzed cells recorded from entorhinal cortex for their tuning to movement direction. During foraging behavior, movement direction differs significantly from head direction. The analysis of neuron responses shows that only 5 out of 758 medial entorhinal cells show significant coding for both movement direction and head direction when evaluating periods of rat behavior with speeds above 10 cm/sec and ±30° angular difference between movement and head direction. None of the cells coded movement direction alone. In contrast, 21 cells in this population coded only head direction during behavioral epochs with these constraints, indicating much stronger coding of head direction in this population. This suggests that the movement direction signal required by most grid cell models may arise from other brain structures than the medial entorhinal cortex.

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Section: Discussionmentioning

confidence: 99%

Head direction is coded more strongly than movement direction in a population of entorhinal neurons

et al. 2015

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“…Many grid cells also exhibit spatially specific responses when recorded on 1D tracks of various shapes (Derdikman et al, 2009; Yoganarasimha et al, 2011; Newman et al, 2014; Gupta et al, 2014; Lipton et al, 2007). On linear 1D tracks, the spatial tuning curves of grid cells consist of multiple firing fields with non-periodic spacing and a large range of field heights (Figures 1D and 1E).…”

Section: Introductionmentioning

confidence: 99%

“…Comput. Neurosci., abstract; Yoganarasimha et al, 2011; Newman et al, 2014; Mathis et al, 2015; Hayman et al, 2011; Gupta et al, 2014; G. Ginosar et al, 2015, Soc. Neurosci., abstract).…”

Section: Introductionmentioning

confidence: 99%

Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice

Yoon

Lewallen

Kinkhabwala

et al. 2016

Neuron

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SUMMARY Grid cells, defined by their striking periodic spatial responses in open 2D arenas, appear to respond differently on 1D tracks: the multiple response fields are not periodically arranged, peak amplitudes vary across fields, and the mean spacing between fields is larger than in 2D environments. We ask whether such 1D responses are consistent with the system’s 2D dynamics. Combining analytical and numerical methods, we show that the 1D responses of grid cells with stable 1D fields are consistent with a linear slice through a 2D triangular lattice. Further, the 1D responses of comodular cells are well described by parallel slices, and the offsets in the starting points of the 1D slices can predict the measured 2D relative spatial phase between the cells. From these results, we conclude that the 2D dynamics of these cells is preserved in 1D, suggesting a common computation during both types of navigation behavior.

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“…We do not introduce phasic noise into persistent spiking cells or VCOs, thereby eliminating significant noise sources for the grid and place cell system. Additionally, we treat place fields as a superposition of three underlying grid fields, which may not be the case especially given evidence from trajectory-dependent place cell (Wood et al, 2000) and grid cell firing (Lipton et al, 2007) and grid field fragmentation (Derdikman et al, 2009; Gupta et al, 2013). In these experiments, animals perform a cognitive task in 1-D mazes, which confer different cognitive demands than foraging behavior in 2-D environments.…”

Section: Discussionmentioning

confidence: 99%

Modeling of grid cell activity demonstrates in vivo entorhinal ‘look-ahead’ properties

Gupta¹,

Erdem²,

Hasselmo³

2013

Neuroscience

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Recent in vivo data show ensemble activity in medial entorhinal neurons that demonstrates ‘look-ahead’ activity, decoding spatially to reward locations ahead of a rat deliberating at a choice point while performing a cued, appetitive T-Maze task. To model this experiment's look-ahead results, we adapted previous work that produced a model where scans along equally probable directions activated place cells, associated reward cells, grid cells, and persistent spiking cells along those trajectories. Such look-ahead activity may be a function of animals performing scans to reduce ambiguity while making decisions. In our updated model, look-ahead scans at the choice point can activate goal-associated reward and place cells, which indicate the direction the virtual rat should turn at the choice point. Hebbian associations between stimulus and reward cell layers are learned during training trials, and the reward and place layers are then used during testing to retrieve goal-associated cells based on cue presentation. This system creates representations of location and associated reward information based on only two inputs of heading and speed information which activate grid cell and place cell layers. We present spatial and temporal decoding of grid cell ensembles as rats are tested with perfect and imperfect stimuli. Here, the virtual rat reliably learns goal locations through training sessions and performs both biased and unbiased look-ahead scans at the choice point. Spatial and temporal decoding of simulated medial entorhinal activity indicates that ensembles are representing forward reward locations when the animal deliberates at the choice point, emulating in vivo results.

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Medial Entorhinal Grid Cells and Head Direction Cells Rotate with a T-Maze More Often During Less Recently Experienced Rotations

Cited by 15 publications

References 57 publications

Head direction is coded more strongly than movement direction in a population of entorhinal neurons

Head direction is coded more strongly than movement direction in a population of entorhinal neurons

Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice

Modeling of grid cell activity demonstrates in vivo entorhinal ‘look-ahead’ properties

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