DOI: 10.5061/dryad.v9s4mw78c
title: Data from: Population analyses reveal heterogenous encoding in the medial prefrontal cortex during naturalistic foraging

description: Foraging in the wild requires coordinated switching of critical functions,
 including goal-oriented navigation and context-appropriate action
 selection. Nevertheless, few studies have examined how different functions
 are represented in the brain during naturalistic foraging. To address this
 question, we recorded multiple single-unit activities from the medial
 prefrontal cortex (mPFC) of rats seeking a sucrose reward in the presence
 of an unpredictable attack posed by a robotic predator (Lobsterbot).
 Simultaneously recorded ensemble activities from neurons were analyzed in
 reference to various behavioral indices as the animal moved freely across
 the foraging area (F) between the nest (N) and the goal (E) area. An
 artificial neural network, trained with simultaneously recorded neural
 activity, estimated the rat’s current distance from the Lobsterbot. The
 accuracy of distance estimation was the highest in the middle F-zone in
 which the dominant behavior was active navigation. The spatial encoding
 persisted in the N-zone when non-navigational behaviors such as grooming,
 rearing, and sniffing were excluded. In contrast, the accuracy decreased
 as the animal approached the E-zone, when the activity of the same
 neuronal ensembles was more correlated with events related to dynamic
 decision-making between food procurement and Lobsterbot evasion. A
 population-wide analysis confirmed highly heterogeneous encoding by the
 region. To further assess the decision-related activity in the E-zone, a
 naïve Bayesian classifier was trained to predict the success and failure
 of avoidance behavior. The classifier predicted the avoidance outcome as
 much as 6 s before the head withdrawal. In addition, two sub-populations
 of recorded units with distinct temporal dynamics contributed differently
 to the prediction. These findings suggest that an overlapping population
 of mPFC neurons may switch between two heterogenous modes, encoding
 relevant locations for goal-directed navigation or an imminent situational
 challenge.
description: # Data from: Population analyses reveal heterogenous encoding in the
 medial prefrontal cortex during naturalistic foraging
 [https://doi.org/10.5061/dryad.v9s4mw78c](https://doi.org/10.5061/dryad.v9s4mw78c) # File Structure 1. **raw.zip**: Raw session data 2. **FineDistanceDataset.zip**: Data for training and evaluating the ANN regressor for distance decoding and PCA analysis 3. **EventClassificationData_4C.zip**: Data for training and evaluating the Naive Bayesian classifier 4. **BehaviorData.zip**: Raw behavior data ## raw * Each folder represents one experimental session. * The recorded region is indicated in the session name (PL or IL). * `*.csv`: Head tracking data. Columns represent frame, row, column, and head angle. * `FPS.txt`: Frame rate (FPS) of the recorded video. * `/recording`: Contains MATLAB data files for each recorded and sorted unit. * `SU` (matrix) : raw unit data * Col1: Fired time\ Col2: Tetrode ID\ Col3: Cell ID\ Col4–Col120: waveform * TDT raw session files (you can use TDT MATLAB SDK to extract data) * `.tbk`: Tank Block file; contains block structure and metadata * `.tdx`: Tank Index file; index for fast data lookup * `.tin`: Tank Info file; stores event/note information * `.tnt`: Tank Note file; contains user notes and annotations * `.tsq`: Tank Sequence file; header information for each data chunk (timestamps, channel info, event codes) ## FineDistanceDataset * Each folder represents one experimental session. * `*buttered.csv`: Head tracking data. Columns represent frame, row, column, and head angle. * `*wholeSessionUnitData.csv`: Normalized neural data. Each row represents a time bin; each column represents a unit. * `*.mat`: MATLAB data file containing frame numbers and corresponding timestamps. * `frameNumber` (matrix) : Frame number * `frameTime` (matrix) : Time in second for each frame * `FPS.txt`: Frame rate (FPS) of the recorded video. * `FI_rank.csv`: Not used. ## EventClassificationData_4C * Each MATLAB data file represents one session. * `X`: Serialized neural data (events × features). * `y`: Labels for each event. 1 = AW HE, 2 = EW HE, 3 = AW HW, 4 = EW HW. ## BehaviorData * Each MATLAB data file represents one session. * `Trials` (matrix) : Onset and offset of each trial in seconds. * `Licks`(matrix) : Onset and offset of all Lick events in seconds. * `Attacks`(matrix) : Onset and offset of all robot attack events in seconds. * `IRs` (matrix) : Onset and offset of all IR beam break events in seconds (animal inside the E-zone) * `ParsedData` (cell) : Contains all events with timestamps adjusted relative to trial onset rather than session onset. For instance, if the second trial started at 120 s and there was a lick event at 124 s, this lick event's time is adjusted to 4 s. Row of the cell represents Trials. * Col1: Trial onset and offset (absolute time) \ Col2: IR events (relative to trial onset) \ Col3: Lick events (relative to trial onset) \ Col4: Attack events (relative to trial onset)