Auditory cortex single unit population activity during natural sound presentation -- dataset

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.

The authors request that any publication using this data cite the following work: https://www.biorxiv.org/content/10.1101/2022.06.10.495698v2

Data format/description

Neural data are stored in two files. All recordings were performed during presentation of the same natural sound library.

• recordings/A1_NAT4_ozgf.fs100.ch18.tgz - data from 849 A1 single units and log spectrogram of stimuli aligned with spike times. 
• recordings/PEG_NAT4_ozgf.fs100.ch18.tgz - data from 398 PEG single units and log spectrogram of stimuli aligned with spike times. 
• wav.zip - raw wav files. Note: Only first 1-sec of each wav file was presented during experiments. Recordings have longer duration

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.

Funding

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