Barry et al. (2007)
Barry, C., Hayman, R., Burgess, N., & Jeffery, K. J. (2007). Experience-dependent rescaling of entorhinal grids. Nature Neuroscience, 10(6), 682–684.
Grid cell recordings from rats foraging in a set of deformable environments, used to show that entorhinal grid patterns rescale with the size and shape of a familiar enclosure. Each rat was recorded over multiple days; a session consists of five 20-minute trials that always began and ended in the familiar environment (e.g. large square, vertical rectangle, small square, horizontal rectangle, large square).
Data are provided as two compressed archives — multiple grid saved_mat.7z (sessions with multiple grid cells) and single cell saved_mat.zip (single-cell sessions) — each containing MATLAB (v7) .mat files, one per session. Session files are named by animal and date — e.g. 407_06-11-10 is rat 407 recorded on 10 November 2006. Two Excel files list the sessions in each set, and produce_mat_for_ila.m shows how the .mat files were generated. Note: the .7z archive needs a 7-Zip-compatible extractor (e.g. 7-Zip, Keka, The Unarchiver).
Each .mat file holds one session. The main variables are:
tint — raw data structure, indexed by trial, holding position (tint.pos.xy, .dir, .speed, sampled at 50 Hz) and per-tetrode spike timing (tint.tetrode.id, .pos_sample).
shapeSeq — order of environments experienced (hr = horizontal rectangle, vr = vertical rectangle, ls = large square, ss = small square).
nPutativeGC — upper-bound count of grid cells in the session.
tets / cells — tetrode number and cluster ID for each putative grid cell.
cutTet1–cutTet4 — per-tetrode, per-trial cluster allocations for every spike.
exposureNo — day within the experimental sequence (rescaling reduces with more exposure).
A full walkthrough, with worked examples for recovering a cell’s spikes and the rat’s position at each spike, is in the included “Description of files.docx”.
Ólafsdóttir et al. (2016)
Ólafsdóttir, H. F., Carpenter, F., & Barry, C. (2016). Coordinated grid and place cell replay during rest. Nature neuroscience, 19(6), 792-794.
All data is recorded using tetrodes. 8 tetrodes in MEC (deep layers) and 8 in hippocampus. Data are acquired using DACQ system from Axona Ltd.
For all recording files (bar data from R2141), tetrodes 1-8 are from MEC. For R2142 it’s the other way around.
Filenames indicate data of recording, animal ID and whether file contains recordings from the Z-track or sleep session. For example, 20151201_R2337_track1, indicates this file is from animal R2337, on the Z-track i.e. track1 and this recording is from 1 Dec 2015.
Sleep recordings end with ‘sleepPOST’ rather than ‘track1’ and were recorded immediately after animals were exposed to the track.
‘Training’ indicates that a recording was made in the 1m square open field.
All files ending with ‘cut’ have spike sorted data.
e.g. 20151127_R2337_track1_11.cut, contains the spike sorted data for tetrode 11
Files ending with .pos contain position data (50Hz)
Files ending with .egf contain LFP data (4.8kHz)
Files ending with .set contain the header
Files ending with a number contain tetrode data
e.g. ‘20151127_R2337_track1.1’ contains tetrode data from tetrode 1.
Some folder have .clu files – the output of KlustKwik – do not use these, they are superseded by the .cut files.
Tanni et al. (2022)
Tanni, S., De Cothi, W., & Barry, C. (2022). State transitions in the statistically stable place cell population correspond to rate of perceptual change. Current Biology, 32(16), 3505-3514.
The hippocampus occupies a central role in mammalian navigation and memory. Yet an understanding of the rules that govern the statistics and granularity of the spatial code, as well as its interactions with perceptual stimuli, is lacking. We analyzed CA1 place cell activity recorded while rats foraged in different large-scale environments. We found that place cell activity was subject to an unexpected but precise homeostasis—the distribution of activity in the population as a whole being constant at all locations within and between environments. Using a virtual reconstruction of the largest environment, we showed that the rate of transition through this statistically stable population matches the rate of change in the animals’ visual scene. Thus, place fields near boundaries were small but numerous, while in the environment’s interior, they were larger but more dispersed. These results indicate that hippocampal spatial activity is governed by a small number of simple laws and, in particular, suggest the presence of an information-theoretic bound imposed by perception on the fidelity of the spatial memory system.
Shipley et al. (2026)
Shipley, S., Abrate, M. P., Hayman, R., Chan, D., & Barry, C. (2026). Disrupted hippocampal replay is associated with reduced offline map stabilization in an Alzheimer’s mouse model. Current Biology.
In vivo CA1 pyramidal cell recordings from 15 male APP NL-G-F/Chat-Cre mice performing a spatial memory task on an 8-arm radial maze, used to examine hippocampal replay and offline map stabilisation in an Alzheimer’s disease mouse model. Recordings were made between 2019 and 2022 using OpenEphys with tetrode arrays (16 tetrodes, 64 channels). Data were spike-sorted using Kilosort 2.0 and manually refined in Phy. Spike times, cluster allocations, and cluster information are provided in NPY format. Accompanying Excel files document animal genotype and task performance.