Environmental deformations dynamically shift the grid cell spatial metric

Abstract

In familiar environments, the firing fields of entorhinal grid cells form regular triangular lattices. However, when the geometric shape of the environment is deformed, these time-averaged grid patterns are distorted in a grid scale-dependent and local manner. We hypothesized that this distortion in part reflects dynamic anchoring of the grid code to displaced boundaries, possibly through border cell-grid cell interactions. To test this hypothesis, we first reanalyzed two existing rodent grid rescaling datasets to identify previously unrecognized boundary-tethered shifts in grid phase that contribute to the appearance of rescaling. We then demonstrated in a computational model that boundary-tethered phase shifts, as well as scale-dependent and local distortions of the time-averaged grid pattern, could emerge from border-grid interactions without altering inherent grid scale. Together, these results demonstrate that environmental deformations induce history-dependent shifts in grid phase, and implicate border-grid interactions as a potential mechanism underlying these dynamics.

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network

“…But over the course of a simulation, the grid-like pattern on the neural sheet drifts [38] such that for a given track position, the corresponding bump locations on the neural sheet slowly change. This drift introduces errors in path-integration [49], which are believed to be corrected by allocentric input from border cells [50,51], boundary vector cells [52], or landmark cells [53,54]. Thus, we implement brief allocentric corrections in our model.…”

“…The grid-like pattern of excitatory drive during brief allocentric corrections ( Figure 2B) represents inputs from neurons with allocentric responses, such as border, boundary vector, or landmark cells [52][53][54]. Interactions between allocentric neurons and grid cells have also been implemented in other continuous attractor models [50,51]. In our model, allocentric input is learned in an accelerated manner during simulation setup.…”

Neural dynamics indicate parallel integration of environmental and self-motion information by place and grid cells

“…Hardcastle et al (2015) suggested, based on the results of their experiment, that path integrating grid cells get reset near borders by direct inputs from border cells. The suggested mechanism was recently implemented in the model of Keinath et al (2018). However, it seems unable to replicate the Gothard et al (1996b) data as realignment occurs only near the end on all track lengths in their model.…”

“…We test our hypothesised model against experimental data on place cell firing in situations where sensory and self-motion information are put into conflict (Gothard et al, 1996b;Redish et al, 2000). The Gothard et al experiment was previously simulated by Sheynikhovich et al (2009) using a model that integrates visual and self-motion information in its grid cell population, and recently by Keinath et al (2018) using a model in which path integrating grid cells receive direct input from border cells. Importantly, however, these single-layer attractor models could not capture the neural dynamics observed in the experiment, as we discuss below.…”

Entorhinal velocity signals reflect environmental geometry

“…However, recent work has revealed that grid cells do not provide an invariant spatial metric across all environments and instead grid patterns deform, distort, and rescale in response to the geometric shape of local environments [8][9][10][11] . Sensory landmark cues, such as environmental boundaries, play a key role in driving such structural changes to grid patterns [12][13][14] . It remains unknown, however, whether velocity signals show flexibility in their coding in response to metric changes to the environment, or contribute to environmentally driven changes in grid patterns.…”