Multi-channel auditory cortex electrophysiology in squirrel monkey

Neural Spike Recordings from Auditory Cortex

This dataset contains multi-unit spike recordings from the auditory cortex of Squirrel monkeys, collected during passive listening tasks. Monkeys were presented with a variety of auditory stimuli, but this documentation focuses on two key stimulus classes used in our study:

- TIMIT: English sentences from the TIMIT corpus

- mVocs: Monkey vocalizations (e.g., grunts, screams)

 📁 Dataset Structure

The dataset is organized as follows:

root/

├── stimuli/ # Metadata about the auditory stimuli

├── sessions/ # One subdirectory per recording session

│ ├── 180413/

│ │ ├── *_MUsp.mat # One file per recording channel

│ │ └── ... # Additional channel files

│ ├── 180420/

│ └── ... # More sessions

├── session_metadata.yml # Annotations about sessions (location, area, hemisphere, etc.)

├── LICENSE.txt # Dataset license

└── README.md # This file

🧠 Notes

- stimuli/ contains:

  - out_sentence_details_timit_all_loudness.mat

  - SqMoPhys_MVOCStimcodes.mat

  - MonkVocs_15Blocks.wav

- Each session directory (e.g., 180413/) contains:

  - Multiple '*_MUsp.mat' files (each corresponding to a recording channel)

- 'session_metadata.yml' includes:

  - Session-level annotations (brain area, hemisphere, bad session exclusions)

  - Stimulus repetition counts

  - 2D coordinates for each session

📄 Description of *_MUsp.mat Files

Each *_MUsp.mat file contains spike data from one recording channel. The following variables are populated when the file is loaded:

🔢 'spike' struct

The spike struct contains spike-level data. Key fields:

- events: s × t matrix of spike waveforms  

  - s: number of detected spikes  

  - t: number of samples per waveform  

- spktimes: vector of spike times (in seconds from recording start)

- amStimcode, fmStimcode, dmrStimcode, mVocStimcode, timitStimcode: vectors of length s, each specifying the stimulus played at the time of the spike

- trial: vector of length `s`, mapping each spike to the trial in which it occurred

 🧪 'trial' struct

The 'trial' struct contains trial-level metadata. Key fields:

- stimon: vector of stimulus onset times (in seconds)

- amStimcode, fmStimcode, dmrStimcode, mVocStimcode, timitStimcode: stimulus code vectors, one per trial

> Note: All stimulus and trial IDs follow MATLAB-style indexing (i.e., start from 1).  

> When working in Python, make sure to adjust for zero-based indexing if needed.

🎧 TIMIT Stimuli

TIMIT stimuli consist of English sentences used during stimulus playback.

- Metadata is stored in: stimuli/out_sentence_details_timit_all_loudness.mat

- Main variable: sentdet — a list of structs (one per stimulus)

Each element in sentdet contains the following key fields:

- sound: waveform (numpy array)

- soundf: sampling rate (Hz)

- duration: total stimulus duration including silence (in seconds)

- befaft: tuple (bef, aft) specifying silence before and after the sentence

🐒 mVocs Stimuli

Monkey vocalizations (e.g. grunts, screams) were played as naturalistic stimuli.

- stimuli/SqMoPhys_MVOCStimcodes.mat  

  - mVocsStimCodes: list of stimulus IDs (MATLAB indexing)

- stimuli/MonkVocs_15Blocks.wav  

  - A .wav file concatenating all monkey vocalizations with silent gaps

📜 License

This dataset is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

📄 Publications Using This Dataset

This dataset has been used in the following studies:

- Ahmed, B. et al. (2025)  

  Deep Neural Networks Explain Spiking Activity in Auditory Cortex.

  PLOS Computational Biology (In press)

- Downer, J. D., Bigelow, J., Runfeldt, M., & Malone, B. J. (2021)

  Temporally Precise Population Coding of Dynamic Sounds by Auditory Cortex

  Journal of Neurophysiology

🔗 Citation

If you use this dataset, please cite the dataset itself:

> Multi-channel auditory cortex electrophysiology in squirrel monkey  

> doi:10.5281/zenodo.16175377