About bst-2

Summary of the data

The accompanying neural data, sounds, and models are outlined in the publication:

Mina Sadeghi, Xiu Zhai, Ian H. Stevenson, Monty A. Escabí, A Neural Ensemble Correlation Code for Sound Category Identification. PLOS Biology (in press).

The neural data consists of multi-trial recordings of analog multi-unit activity (aMUA) measured from 16-channel linear recording arrays in the auditory midbrain (inferior colliculus) of unanesthetized rabbits listening to natural sounds. The sound delivery consisted of three paradigms as described in detail in the manuscript. Paradigm 1 consist of five unaltered natural sound textures: fire, water, crowd noise, bird chorus, and a rattling snake sound. Paradigm 2, consists of the same sounds as Paradigm 1 along with perturbed variants with equalized power spectrum. For paradigm 3, 6 sound exemplars were selected and delivered from three sound categories (water, fire, and human speech). All of the sounds used along with MATLAB code to estimate multi-site neural correlations, as described in the manuscript, are provide.

We also include MATLAB code used to measure time-varying correlations using an auditory model representation along with the database of sounds used for classification of sound categories, as described in the manuscript.

Format of the data

The neural response data is stored in Matlab files and the sound stimuli are stored in .wav files.  Details are given in the document linked to at the bottom of this page. Total size of the data set is about 5 GB.

Conditions for using the data

If you use this dataset or any of the accompanying analysis or modeling code please cite the above manuscript along with a citation to the dataset:

Mina Sadeghi, Xiu Zhai, Ian H. Stevenson, Monty A. Escabi; (2019). Multi-site neural recordings in the auditory midbrain of unanesthetized rabbits listening natural texture sounds and sound correlation auditory models. CRCNS.org

http://dx.doi.org/10.6080/K03X84V3

The above citation uses a Digital Object Identifier (DOI) which is assigned to the data set.  The DOI was created using DataCite (www.datacite.org) and the California Digital Library, "EZID" system (ezid.cdlib.org).

How to download the data

Data may be downloaded from:
https://portal.nersc.gov/project/crcns/download/bst-2
A CRCNS.org account is required. See the download link for more instructions.

Getting help using the data

If you have questions about using the data, please post them on the forum for using data sets.

Documentation files

crcns_bst-2_data_description.pdf - (Data description)
FigureData.pdf - (List of data files that were used to generate all of the figure panels in the manuscript).