Supplementary MaterialsFigure 1source data 1: Cue score and shuffle distributions

Supplementary MaterialsFigure 1source data 1: Cue score and shuffle distributions. 1: Cue scores, bilateral scores and percentages of left and right cue cells. elife-43140-fig5-data1.mat (797K) GUID:?C9343D32-C5AE-423A-9D98-0DFE7422FDB2 Physique 5figure supplement 2source data 1: Cue scores, spatial shifts and both-side cue template. elife-43140-fig5-figsupp2-data1.mat (7.1K) GUID:?DD5FB6CC-65EE-4E39-A35B-CA582EDC3FF6 Physique 5figure supplement 4source data 1: Cue scores, bilateral scores and percentages of cue cells in layers 2 and 3 on a 18-meter virtual linear track. elife-43140-fig5-figsupp4-data1.mat (5.2M) GUID:?A42FA346-4C20-4F23-90DE-31048E8543CA Physique 6source data 1: Percentages of common cue and non-cue cells, and spatial shifts of common cue cells in DBPR108 different environments. elife-43140-fig6-data1.mat (623 bytes) GUID:?D2B937AC-83D9-4A91-8A2E-48390601240A Transparent reporting form. elife-43140-transrepform.pdf (337K) GUID:?E3EE6931-D5B1-4145-82B9-306B90E31FFB Data Availability StatementAll data generated or analyzed during this study are DBPR108 included in the manuscript and supporting files. Abstract During spatial navigation, animals use self-motion to estimate positions through path integration. However, estimation errors accumulate over time and it is unclear how they are corrected. Here we report a new cell class (cue cell) encoding visual cues that could be used to correct errors in path integration in mouse medial entorhinal cortex (MEC). During virtual navigation, individual cue cells exhibited firing fields only near visual cues and their populace response created sequences repeated at each cue. These cells consistently responded to cues across multiple environments. On a track with cues on left and right sides, most cue cells only responded to cues on one side. During navigation in a real arena, they showed spatially stable activity and accounted for 32% of unidentified, spatially stable MEC cells. These cue cell properties demonstrate that this MEC contains a code representing spatial landmarks, which could be important for error correction during path integration. track) and, in the second track, the last three cues were removed (track). Tetrode recordings were performed as mice ran along both forms of track in blocks of trials within the same session. Water rewards were delivered in the same location on each RGS19 track regardless of the cue location differences. At the beginning of both and songs, where the songs were identical, the spatial firing prices of cue cells had been similar across monitors. Vertical rings of spikes had been within the run-by-run raster plots of both monitors and produced peaks within the spatial firing price. The rings had been defined as spatial firing areas also, which generally aligned to top features of the surroundings (spatial cues/adjustments in wall structure patterns) present on both monitors (Body 2A, crimson lines indicate field places). However, the firing patterns changed DBPR108 dramatically from the real point across the track where in fact the environments begun to differ. Spatial firing areas had been prominent at cue places along the whole remaining area of the monitor (Body 2A, best) but weren’t present on a single area of the monitor (Body 2A, bottom level). To quantify this difference, we performed two computations using the same number of operates for both and monitors (Body 2B and D). In both full cases, the data had been put into two locations along the monitor (best of Body 2B and D): the beginning area where cues had been present for both monitors (black club marks this area, Area A – same) and all of those other monitor where cues had been either present or absent (green club, Area B – different). We computed the Pearson relationship with lags differing from initial ?300 to 300 cm in 5 cm actions. The correlation between the firing rate and template was defined to be the value of the correlation at the peak in the Pearson correlation located closest to zero shift. This correlation was lower for the firing rates on the track in Region B but not for region A (Number 2B, combined one-tailed t-test: correlation in Region B on track? ?correlation in Region B on track, N?=?65, 3 animals, for region A:?p=0.56;?for region B: p=110?14). We also compared changes of the spatial firing field distribution of the cue cell populace (Number 2C) across the two songs and, for each cue cell, the portion of the track region comprising spatial firing fields (Number 2D). In Number 2C, for each 5 cm bin along the track, the portion of the?cue cell?populace having a field in that bin are plotted for the and songs for two environments. In Region A for both songs, cue cells showed a similar portion of the spot with areas. Cue cells acquired spatial firing areas clustered in each area in which a cue was situated on both monitors, but these areas weren’t present when cues had been removed in Area B from the monitor (Amount 2C). In Area B, the small percentage of the spot with spatial firing areas was low in the monitor set alongside the monitor (Amount 2D, matched one-tailed t-test: field small percentage.

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