Data from: Population analyses reveal heterogenous encoding in the medial prefrontal cortex during naturalistic foraging
Data files
Jan 14, 2026 version files 1.39 GB
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BehaviorData.zip
3.49 MB
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EventClassificationData_4C.zip
15.09 MB
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FineDistanceDataset.zip
151.46 MB
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raw.zip
1.22 GB
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README.md
3.05 KB
Abstract
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.
https://doi.org/10.5061/dryad.v9s4mw78c
File Structure
- raw.zip: Raw session data
- FineDistanceDataset.zip: Data for training and evaluating the ANN regressor for distance decoding and PCA analysis
- EventClassificationData_4C.zip: Data for training and evaluating the Naive Bayesian classifier
- 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
- Col1: Fired time
- 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 numberframeTime(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)
- Col1: Trial onset and offset (absolute time)
