Untethered firing fields and intermittent silences: Why grid‐cell discharge is so variable

Abstract

Grid cells in medial entorhinal cortex are notoriously variable in their responses, despite the striking hexagonal arrangement of their spatial firing fields. Indeed, when the animal moves through a firing field, grid cells often fire much more vigorously than predicted or do not fire at all. The source of this trial‐to‐trial variability is not completely understood. By analyzing grid‐cell spike trains from mice running in open arenas and on linear tracks, we characterize the phenomenon of “missed” firing fields using the statistical theory of zero inflation. We find that one major cause of grid‐cell variability lies in the spatial representation itself: firing fields are not as strongly anchored to spatial location as the averaged grid suggests. In addition, grid fields from different cells drift together from trial to trial, regardless of whether the environment is real or virtual, or whether the animal moves in light or darkness. Spatial realignment across trials sharpens the grid representation, yielding firing fields that are more pronounced and significantly narrower. These findings indicate that ensembles of grid cells encode relative position more reliably than absolute position.

Dentate spikes and external control of hippocampal function

“…Indeed, the findings of a hippocampus-wide (Figs. 6, 7), DS M -promoted SG dom control of hippocampus information processing to a non-local mode of information processing identify a source of the overdispersion that is characteristic of place cells in CA1, CA3, and DG (Fenton et al, 2010; Fenton and Muller, 1998; Hok et al, 2012; Jackson and Redish, 2007; van Dijk and Fenton, 2018), and also grid cells, although we cannot conclude the mechanism is the same (Nagele et al, 2020). The findings also offer an explanation for the possible utility of CA1 receiving two spatial inputs; the Schaffer collaterals provide place cell inputs that can be non-local and related to mental experience, whereas the temporoammonic pathway provides an input comprised of components of place (grid cell distances, directional cells, border cells, and speed cells) more tethered to local, physical experience.…”

A universal probabilistic spike count model reveals ongoing modulation of neural variability

“…This irregularity is also seen in continual neural recordings without trial structure [8]. The resulting variability has classically been characterised as ‘Poisson’, with a Fano factor (variance to mean ratio) of one [9], but experimental data also often exhibits significantly more [10, 8, 11, 12] and sometimes less [13, 14] variability, respectively referred to as over- or underdispersion. Moreover, experimental studies have revealed that neural variability generally depends on stimulus input and behaviour [15, 16, 17, 18], and exhibits structured shared variability (‘noise correlations’) across neurons even after conditioning on such covariates.…”

Introduction to part two of the special issue on computational models of hippocampus and related structures

“…Recent models have computed the successor representation between different spatial locations laid out as a discrete array of locations, showing how the eigenvectors of these successor representations can appear similar to grid cells (Stachenfeld, Botvinick, & Gershman, 2017). This provides another framework for addressing the firing properties of grid cells, which have been addressed in other papers in this special issue (Bush & Burgess, 2020; Nagele, Herz, & Stemmler, 2020; Stella et al, 2020). However, the discretization of an array of locations does not easily address the effect of changes in the shape and size of the environment or the position of barriers (Barry, Hayman, Burgess, & Jeffery, 2007; Burgess & O'Keefe, 1996), or the role of spatial context (Zhu, Paschalidis, Chang, Stern, & Hasselmo, 2020).…”