Cortical neuronal populations can use a multitude of codes to represent information, each with different advantages and trade-offs. The auditory cortex represents sounds via a sparse code, which lies on the continuum between a localist representation with different cells responding to different stimuli, and a distributed representation, in which each stimulus is encoded in the relative response of each cell in the population. Being able to dynamically shift the neuronal code along this axis may help with certain tasks that require categorical or invariant representations. Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons may have distinct effects on population neuronal codes, differentially shifting the encoding of sounds between distributed and localist representations. We stimulated optogenetically SST or VIP neurons while simultaneously measuring the response of populations of hundreds of neurons to sounds presented at different sound pressure levels. SST activation shifted the neuronal population responses toward a more localist code, whereas VIP activation shifted them towards a more distributed code. Upon SST activation, sound representations became more discrete, relying on cell identity rather than strength. In contrast, upon VIP activation, distinct sounds activated overlapping populations at different rates. These shifts were implemented at the single-cell level by modulating the response-level curve of monotonic and nonmonotonic neurons. These results suggest a novel function for distinct inhibitory neurons in the auditory cortex in dynamically controlling cortical population codes.

Auditory perception requires categorizing sound sequences, such as speech, into classes, such as syllables. Auditory categorization depends not only on the sequences' acoustic waveform, but also on the listener's sensory uncertainty, any individual sound's relevance to the task, and learning the temporal statistics of the acoustic environment. However, whether and how these factors interact to shape categorization is unknown. Here, we measured human participants' performance on a multi-tone categorization task. Task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, poor estimates of tones' task relevance or high sensory uncertainty adversely impacted category choice. Learning temporal statistics of sound category also affected decisions. The magnitude of this effect correlated inversely with participants' relevance estimates. These results demonstrate that humans differentially weigh sensory uncertainty, task relevance and learning, providing a novel understanding of sensory decision-making under real-life behavioral demands.