Grid scale drives the scale and long-term stability of place maps

Abstract

Medial entorhinal cortex (MEC) grid cells fire at regular spatial intervals and project to the hippocampus, where place cells are active in spatially restricted locations. One feature of the grid population is the increase in grid spatial scale along the dorsal-ventral MEC axis. However, the difficulty in perturbing grid scale without impacting the properties of other functionally-defined MEC cell types has obscured how grid scale influences hippocampal coding and spatial memory. Here, we use a targeted viral approach to knock out HCN1 channels selectively in MEC, causing grid scale to expand while leaving other MEC spatial and velocity signals intact. Grid scale expansion resulted in place scale expansion in fields located far from environmental boundaries, reduced long-term place field stability and impaired spatial learning. These observations, combined with simulations of a grid-to-place cell model and position decoding of place cells, illuminate how grid scale impacts place coding and spatial memory.

Inter- and intra-animal variation of integrative properties of stellate cells in the medial entorhinal cortex

“…Figure 5 of (Stensola et al, 2012) ). Modification of grid field scaling following deletion of HCN1 channels is also consistent with this possibility (Giocomo et al, 2011;Mallory et al, 2018) . Alternatively, inter-animal differences may reflect multiple ways to achieve a common higher order phenotype.…”

Heterogeneity in hippocampal place coding

“…The middle and bottom panels depict the impact on grid and place cell activity, respectively. From left to right: Control recordings in a white rectangular arena; Large changes in environmental context resulted in realignment of the grid pattern and global remapping of place cells [36]; Smaller changes to environmental context altered to varying degrees the firing rate of individual grid nodes and induced rate remapping in place cells [27 • ]; Lesions of MEC that eliminate grid activity have largely resulted in increased place field size (similar effects were seen following muscimol inactivation of MEC, not shown) [38,40,76]; Optogenetic inhibition of MEC greatly reduced grid cell firing and drove remapping in place cells without impacting field size; MEC-specific knockout of HCN1 channels increased the scale of both grid and place cell representations (particularly amongst place fields located far from environmental boundaries) and decreased place cell stability [50 •• ]; Inactivation of medial septum (MS) largely eliminated the periodicity of grid cell firing patterns, with the impact on place cells including minimal effects on stability and large disruptions in the spatial coding of all place cells save those with fields near boundaries [44,45,49,76]. …”

“…The difference in the function coding features of inputs to place cells could underlie the seemingly conflicting reports of how MEC inputs influence both CA1 remapping and the size of place fields. Consistent with this idea, recent work discovered that increasing grid scale through targeted knockdown of HCN1 channels reduced long-term place field stability in a sub-population of CA1 place cells and simultaneously expanded the size of CA1 place fields, with the magnitude of this impact dependent on the distance of a given place field from the nearest environmental boundary [50 •• ]. Thus, it is important to keep in mind that experimental design, such as the availability of proximal or boundary related sensory cues, can have highly variable effects on the coding features of CA1 place cells, and caution should be exercised when drawing conclusions regarding generalizable mechanisms.…”

Awake hippocampal-prefrontal replay mediates spatial learning and decision making

“…Only well isolated neurons with stable spiking waveforms were included (Figure S1). Cluster quality was assessed using isolation distance (Schmitzer-Torbert et al, 2005), cluster center-of-mass shift (Mallory et al, 2018), and spike-waveform correlation (Li et al, 2017) (Figure S1). Cluster center-of-mass shift between two different sessions was calculated as the Mahalanobis distance between the cluster centroids of the same single unit from these sessions.…”

“…For the Wmaze single-day learning paradigm, single-units were tracked continuously across interleaved run and rest sessions (Wmaze run sessions: 17.9 ± 1.0 mins per session, 8 sessions per rat; interleaved rest sessions in rest box: 23.0 ± 4.9 minsper session, 9 sessions per rat; total recording duration: 6.04 ± 0.37 hours per rat; mean ± SD). From top to bottom, isolation distance(Schmitzer-Torbert et al, 2005), cluster center-of-mass shift(Mallory et al, 2018) across sessions (spikes from the first session of the Animal ER1 were clustered separately and therefore excluded from the center-of-mass shift analysis), correlation coefficient of spike waveforms in two consecutive sessions(Li et al, 2017) (Fisher-transformed for normality).Each dot on the scatter plots represents an isolated single unit (All cells that were continuously tracked across all 8 behavioral sessions with stable spiking waveforms, and with firing fields on the W-track, are shown; CA1: n = 216 cells; PFC, n = 154 cells). Dotted horizontal lines indicate inclusion thresholds used for each criterion.…”