DOI: 10.5281/zenodo.8044773
title: Auditory cortex single unit population activity during natural sound presentation -- dataset

description: Overview


High-density multi-channel neurophysiology data were collected from primary (A1) and secondary (PEG) fields of auditory cortex of passively listening ferrets during presentation of a large natural sound library. Single unit spikes were sorted using Kilosort. This dataset includes spike times for 849 A1 units and 398 PEG units. Stimulus waveforms were transformed to log-spaced spectrograms for analysis (18 channels, 10 ms time bins). Data set includes raw sound waveforms as well and high resolution (1000 samples/sec) single-trial spike data. The authors request that any publication using this data cite the following work: https://www.biorxiv.org/content/10.1101/2022.06.10.495698v2


Version 1.1 is updated with more examples and documentation. It also includes a less-processed version of the spike data that permits reconstruction of the experimental sequence used at each recording site and single-trial responses to the repeated validation stimuli.


Data format/description


Preprocessed neural data are aggregated in two main files. All recordings were performed during presentation of the same natural sound library to passively listening  animals. During each experiments, stimuli were presented in a random order, and repeated validation stimuli were interleaved throughout the experiment. In the main files, data have been aligned to the same order by stimulus and averaged across repeated presentations (for the validation stimuli, which were presented 20 times during each experiment). The averaged validation data make up the first 27 seconds of each recoding block.



	
A1_NAT4_ozgf.fs100.ch18.tgz - data from 849 A1 single units and log spectrogram of stimuli aligned with spike times.  Data are aggregated across 64- or 128-channel recordings from 22 sites in 4 animals.
	
PEG_NAT4_ozgf.fs100.ch18.tgz - data from 398 PEG single units and log spectrogram of stimuli aligned with spike times. Data are aggregated across 64-channel recordings from 12 sites in 2 animals.



Raw sound files (44100/s sampling, wav format) and spike times (1K/s sampling, in the original experimental order) are also provided in separate files. Summary data of model performance from the paper are also included.



	
wav.zip - raw wav files. As of version 2 of this repository, the wav files have been truncated to the 1-sec duration that was used in the experiments
	
A1_single_sites.zip, PEG_single_sites.zip - collections of files, one per recording site, with spike times stored in the actual order of data collection (including interleaved repeated validation stimuli). These spikes have been binned at 100 Hz and sorted to have matched order across all sites in the processed files (A1_NAT4_ozgf.fs100.ch18.tgz, PEG_NAT4_ozgf.fs100.ch18.tgz, respectively).
	
A1_pred_correlation.csv, PEG_pred_correation.csv - Comma-separated value file containing cross-validated prediction accuracy for each A1, PEG unit for each of the five exemplar models. The "sig_auditory" column is true for all units classified as having significant auditory responses, as classified in the publication.



Example scripts


Python scripts included with this dataset demonstrate how to load the neural data and perform a CNN model fit. Running the scripts requires the NEMS0 python library, which is available open source at https://github.com/lbhb/NEMS0.


Quick install


Create and activate a new conda environment:




conda create -n NEMS0 python=3.7
conda activate NEMS0



Download NEMS0:




git clone https://github.com/lbhb/NEMS0



Install NEMS0:




pip install -e NEMS0



Detailed instructions for installing NEMS0 are available in the Github repository (https://github.com/lbhb/NEMS0).


Demo scripts


Once NEMS0 is installed and the data are downloaded, move to the directory where the data and demo scripts are stored and run them in a NEMS0 environment.



	
pop_cnn_load.py - Load the A1 data and compare predictions for two neurons (Fig 3) by two population models (stage 1 fit complete). Illustrates how to load the data using Python.
	
pop_cnn_fit.py - Load  a pre-fit A1 population model (stage 1) and complete stage 2 fit (refinement) for a single neuron. Illustrates use of NEMS0 for CNN model fitting.
	
single_trial_demo.py - Script demonstrating how to load the single trial data for a repeated validation stimulus from one A1 neuron. Also how to compute the average population PSTH for a single validation stimulus at 1000 sec-1 sampling. Unzip A1_single_sites.zip in the director containing this script first in order for it to run correctly.



Funding


Data collection, software development and processing were supported by funding from the NIH (R01DC014950, R01EB028155).