DOI: 10.7272/q61n7zc3
title: Data from: Hippocampal replay of experience at real-world speeds

description: Representations related to past experiences play a critical role in memory
 and decision-making processes. The rat hippocampus expresses these types
 of representations during sharp-wave ripple (SWR) events, and previous
 work identified a minority of SWRs that contain “replay” of spatial
 trajectories at ~20x the movement speed of the animal. Efforts to
 understand replay typically make multiple assumptions about which events
 to examine and what sorts of representations constitute replay. We
 therefore lack a clear understanding of both the prevalence and the range
 of representational dynamics associated with replay. Here we develop a
 state space model that uses a combination of movement dynamics of
 different speeds to capture the spatial content and time evolution of
 replay during SWRs. Using this model, we find that the large majority of
 replay events contain spatially coherent, interpretable content.
 Furthermore, many events progress at real-world, rather than accelerated,
 movement speeds, consistent with actual experiences.
description: # Dataset description This dataset contains
 extracellular neural recordings of ten male Long Evans rats (500-700 g,
 4-9 months old) that were trained on a W-track spatial alternation task. 9
 rats contributed to previous studies (Karlsson et al. 2008, 2009, Carr et
 al. 2012, Kay et al. 2016, 2020). Neural activity was recorded using
 tetrodes in CA1, CA2, CA3, MEC, Subiculum, and Dentate Gyrus depending on
 the rat. This dataset has been processed to extract the
 LFP, sharp wave ripple times, clustered cells, multiunit spikes and
 associated spike features, and position of the animal using custom Frank
 lab Matlab code. ### Rats<br> 1.
 Coriander<br> 2. Bond<br> 3. Chapati<br> 4.
 Conley<br> 5. Dave<br> 6. Dudley<br> 7. Egypt<br>
 8. Frank<br> 9. Government<br> 10. Remy The
 data were collected by Dr. Mattias Karlsson (animals Bond, Frank, Conley,
 Dudley), Dr. Margaret Carr (animal Coriander), Dr. Kenneth Kay (animals
 Chapati, Dave), Drs. Kenneth Kay and Marielena Sosa (animal Egypt), Drs.
 Kenneth Kay and Jason E. Chung (animal Government), and Dr. Anna K.
 Gillespie (animal Remy). Data from Drs. Margaret Carr
 and Mattias Karlsson was previously posted at CRCNS:
 http://crcns.org/data-sets/hc/hc-6. Any publications made using that data
 should cite the data set using the following:
 ```<br> Mattias Karlsson, Margaret Carr, Loren M. Frank
 (2015). Simultaneous extracellular recordings from hippocampal areas CA1
 and CA3 (or MEC and CA1) from rats performing an alternation task in two
 W-shaped tracks that are geometrically identically but visually
 distinct.<br> CRCNS.org.
 http://dx.doi.org/10.6080/K0NK3BZJ<br> ``` This
 dataset is open to reuse with citation. Please contact Loren Frank
 (loren@phy.ucsf.edu) with questions about the dataset.
description: # Format of the data ### 1. Overview of
 directory structure There are ten compressed
 directories (*.tar.gz). Each directory contains Matlab files corresponding
 to a single animal. Within each animal directory, there is a subfolder
 named “EEG”. This folder contains the local field potential recordings
 from each tetrode. ### 2. Naming conventions
 All files are named based on the first three letters of the
 animal’s name, followed by the type of data they contain, following by a
 two digit number indicating the day of recording for that file. Thus,
 conspikes02.mat contains spiking data from Animal Conley for day 2 of data
 collection. EEG files (which should be named “LFP” but
 are not for historical reasons) follow a slightly different naming
 convention, where each file consists of the first three letters of the
 animals name, (which we refer to as the animal short name) followed by the
 data type, followed by three numbers separated by dashes which give the
 day of recording, the epoch (defined below) and the tetrode whose data is
 contained in the file. Thus, coneeg02-3-05.mat contains data for day 2,
 epoch 3, tetrode 5. ### 3. Data organization
 All datatypes are stored in nested cell arrays with the same
 organization.<br>   - The first level cell array indexes over
 days.<br>   - The second level cell array indexes over epochs, which
 are contiguous chunks of data where the animal is in one particular
 environment, such as on a track or in a<br>   rest box.<br>  
 - The third level cell array, if it exists, indexes over
 tetrodes.<br>   - The fourth level cell array, if it exists, indexes
 over clustered units (or cells if you<br>   prefer) within a
 tetrode.<br>   spikes{2}{3}{1}{4} would therefore contain data from
 day 2, epoch 3, tetrode 1 and unit 4.<br>   Note that because the
 data are generally stored by day, if you load conspikes02.mat, you will
 get a spike cell array where spikes{1} is empty, and spikes{2} has
 data. ### 3. Data files #####
 {animal}cellinfo.mat #####<br> - **Description**: Defines basic
 measured characteristics of each neuron such as Spike Width and Mean
 Rate.<br> - **Format**:<br>   - 1 x {*n_days*}
 Matlab-cell<br>     - 1 x {*n_epochs*} Matlab-cell<br>       -
 1 x {*n_tetrodes*} Matlab-cell<br>         - 1 x {n_cells}
 Matlab-cell<br>           - Cell Matlab-structure<br>
             - `spikewidth`: duration of spike, in points sampled at 30
 KHz, of spike from peak to trough<br>             - `meanrate`: mean
 rate of spikes (Hz) ##### {animal}spikes{day}.mat
 #####<br> - **Description**: Gives the spike times and other
 relevant information at spike times.<br> - **Format**:<br>   -
 1 x {*n_days*} Matlab-cell<br>     - 1 x {*n_epochs*}
 Matlab-cell<br>       - 1 x {*n_tetrodes*} Matlab-cell<br>
         - Spike Matlab-structure<br>           - fields: fields of
 data array<br>             - `time`: time of spike event in
 seconds<br>             - `x`: x-position of animal at
 spike<br>             - `y`: y-position of animal at spike<br>
             - `dir`: head direction at spike<br>             -
 `amplitude`: amplitude of highest variance channel<br>             -
 `x-sm`: smoothed x-position of animal at spike<br>             -
 `y-sm`: smoothed y-position of animal at spike<br>             -
 `dir-sm`: smoothed head direction at spike #####
 {animal}rawpos{day}.mat #####<br> - **Description**: Raw position
 data collected from the ccd camera. ##### **Name**:
 {*animal*}pos{*n_days*}.mat #####<br> - **Description**: Position of
 animal on track. Derived from the rawpos data structures. Timestamps are
 in seconds.<br> - **Format**:<br>   - 1 x {*n_days*}<br>
       - 1 x {*n_epochs*}<br>           - position
 structure<br>               - arg: arguments used to derive the
 position structure from the rawpos information<br>               -
 descript: description of the data in the position structure<br>
               - fields: labels for the data array<br>              
 - data: array with field labels<br>                   - `time`: time
 in session in seconds<br>                   - `x`: x-position of
 animal (cm)<br>                   - `y`: y-position of animal
 (cm)<br>                   - `dir`: head-direction of
 animal<br>                   - `vel`: velocity of animal (cm /
 s)<br>                   - `x-sm`: smoothed x-position of animal
 (cm)<br>                   - `y-sm`: smoothed y-position of animal
 (cm)<br>                   - `dir-sm`: smoothed head-direction of
 animal<br>                   - `vel-sm`: smoothed velocity of animal
 (cm / s)<br>               - `cmperpixel`: - centimeters per pixel,
 scales the ccd camera pixels to centimeters #####
 {*animal*}linpos{*n_days*}.mat #####<br> - **Description**: Gives
 the animal's distance from a well (either at the starting position
 well or at the end of one of the arms). Derived from the pos data
 structure. ##### {*animal*}task{*n_days*}.mat
 #####<br> - **Description**: Defines the different task epochs for a
 single experimental session<br> - **Format**:<br>   - 1 x
 {*n_days*} Matlab-cell<br>       - 1 x {*n_epochs*}
 Matlab-cell:<br>           - task epoch Matlab-structure<br>
               - `Type` - type of epoch (sleep, run, rest)<br>
               - `Linear coord`: these are coordinates relative to user
 specified endpoints of track segments (only there if relevant to task
 epoch).<br>               - `Environment`: type of running
 track<br>                   -  `lin`: linear track<br>
                   -  `wtr1`: w-track<br>                   - 
 `postsleep`: after sleeping<br>                   -  `presleep`:
 before sleeping ##### {*animal*}DIO{*n_days*}.mat
 #####<br> - **Description**: The *DIO* cell gives arrival/departure
 times at the end of each arm of the maze (as indicated by the IR motion
 sensors at the end of the wells) and the start/stop times for the output
 trigger to the reward pump. Timestamps are in 100 µsec units.<br> -
 **Format**:<br>   - 1 x {*n_days*}<br>       - 1 x
 {*n_epochs*}<br>           - 1 x {number of DIO-board
 pins}<br>               - non-empty cells correspond to active
 pins<br>               - there are two active pins for each reward
 well corresponding to either the IR motion sensor or the reward
 pump<br>               - DIO structure<br>                   -
 `pulsetimes`: start/stop time of activation<br>                   -
 `timesincelast`: time since last activation<br>                   -
 `pulselength`: duration of activation<br>                   -
 `pulseind`: index of pulse #####
 {*animal*}ripples{*n_days*}-{epoch}-{tetrode}.mat #####<br> -
 **Description**: First processing step in the ripple identification
 process. Contains the ripples after the bandpass filtering on a single
 tetrode<br> - **Format**: #####
 {*animal*}ripples{*n_days*}.mat #####<br> - **Description**:
 Intermediate processing step in the ripple identification process.
 Contains the ripples after the bandpass filtering, z-scoring, thresholding
 and segmenting the ripple on a single tetrode<br> -
 **Format**:<br>     - 1 x {*n_days*} Matlab-cell<br>         -
 1 x {*n_epochs*} Matlab-cell<br>             - 1 x {*n_tetrodes*}
 Matlab-cell<br>                 - `startind`: index of the start
 time of each ripple<br>                 - `endind`: index of the end
 time of each ripple<br>                 - `midind`: index of of the
 midpoint time of each ripple<br>                 - `startime`: start
 time of the ripple<br>                 - `endtime`: end time of the
 ripple<br>                 - `midtime`: mid-point time of the
 ripple<br>                 ...<br> #####
 {*animal*}candripples{*n_days*}.mat #####<br> - **Description**:
 Final processing step in the ripple identification process. Contains the
 ripples after combining tetrodes<br> - **Format**:<br>     - 1
 x {*n_days*} Matlab-cell<br>         - 1 x {*n_epochs*}
 Matlab-cell ##### {*animal*}marks{*n_days*}.mat
 #####<br> - **Description**:<br> - **Format**:<br>   - 1
 x {*n_days*} Matlab-cell<br>       - 1 x {*n_epochs*}
 Matlab-cell<br>           - 1 x {*n_tetrodes*} Matlab-cell<br>
             - Marks Matlab structure<br>               - `data`,
 (n_time, n_mark_features)<br>                 - columns correspond
 to spike amplitudes at time of spike for each tetrode wire
 ##### {*animal*}tetinfo.mat #####<br> - **Description**:
 Gives basic information about each tetrode such as the depth and number of
 neurons recorded, lists the valid electrodes<br> -
 **Format**:<br>     - 1 x {*n_days*} Matlab-cell<br>         -
 1 x {*n_epochs*} Matlab-cell<br>             - 1 x {*n_tetrodes*}
 Matlab-cell<br>                 - Tetrode Matlab-structure<br>
                     - `depth`: depth of electrode, if specified, this is
 the number of 1/12ths of a turn of a 0x80 (80 threads / inch) screw from
 the bottom of the microdrive at which these data were collected. To
 convert to mm, multiply by 0.0265.<br>                     -
 `numcells`: number of neurons recorded on this tetrode<br>
                     - `descrip`: Identifies the type of tetrode - riptet,
 {brain_area}REF<br>                     - `area`: brain
 area #####
 {*animal*}eeg{*n_days*}-{*epoch*}-{*tetrode*}.mat #####<br> -
 **Description**: Gives the recorded local field potential for a
 tetrode.<br> - **Format**:<br>     - 1 x {*n_epochs*}
 Matlab-cell<br>         - 1 x {*n_tetrodes*} Matlab-cell<br>
             - EEG Matlab-structure<br>                 - `descript`
 - timestamps in hrs:min:sec<br>                 - `fields` - fields
 of data array<br>                 - `starttime` - time of first data
 sample (in seconds)<br>                 - `samprate` - sampling rate
 in Hz<br>                 - `data` - data array with columns
 corresponding to fields<br>                 - `depth` - depth of
 electrode<br> #####
 {*animal*}eeggrnd{*n_days*}-{*epoch*}-{*tetrode*}.mat #####<br> -
 **Description**: Gives the LFP for a given ground tetrode (Ground wire
 located in the corpus collosum)<br> - **Format**:<br>     - 1
 x {*n_epochs*} Matlab-cell<br>         - 1 x {*n_tetrodes*}
 Matlab-cell<br>             - EEG Matlab-structure<br>
                 - `descript` - timestamps in hrs:min:sec<br>
                 - `fields` - fields of data array<br>
                 - `starttime` - in seconds<br>                 -
 `samprate` - sampling rate in Hz<br>                 - `data` - data
 array<br>                 - `depth` - depth of electrode