Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex -- Dataset

This repository contains data from Wingert, JC, Parida, S, Norman-Haignere, SV, David, SV. (2026) Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex. Nature Neuroscience. In press.

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 (50-400 18-sec sound sequences per experiment). This dataset includes spike times for 2128 A1 units and 746 PEG units, collected from 64 recording sites in four animals. Stimulus waveforms were transformed to log-spaced spectrograms for analsysis (32 channels, 200-20000 Hz, 10 ms time bins). The data set also includes raw sound waveforms used in the study (44kHz sampling).

Scripts are included to demonstrate methods for measuring and visualizing tuning subspaces from CNN-based encoding models trained on the sound-evoked neural activity.

Further details in the README file.