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