Task-specific invariant representation in auditory cortex

Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.

Methods
Neural spiking activity was recorded from the auditory cortex of ferrets while animals engaged in an auditory detection task. Data was acquired using laminar silicon multi-electrode arrays acutely inserted into the auditory cortex region of interest (A1 or dPEG). Raw data was spike sorted using Kilosort2, followed by manual curation in Phy. For details on the spike sorting procedure or on the experimental set up in general, please refer to the associated eLife manuscript.

For the purposes of sharing this data, we have included the post-spike sorted data for all electrophysiology experiments discretized into spike counts at 10 Hz and 50 Hz sampling, as these were the two views of the data we used to generate all analyses in the manuscript. These data were saved using the NEMS recording object format which can be easily loaded and manipulated in Python using the NEMS library (https://github.com/LBHB/NEMS). These recording objects contain additional information about the animal's behavior on a each experimental trial, allowing investigation of the relationship between sound-evoked neural activity and behavior.

In addition to the raw neural data, we have also included cached versions of various data analysis stages. For example, we have included files that contain the results from our neural decoding analysis for each experiment. We would like to refer those interested to our github repository: https://github.com/crheller/eLife2024_Task which provides instructions for installing the necessary Python code to rerun all analyses and re-produce the eLife manuscript figures.