The function of cortical gain adaptation in detecting sounds in noise.

University Of Pennsylvania, Philadelphia PA

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Abstract

PROJECT SUMMARY The function of cortical gain adaptation in detecting sounds in noise. Natural acoustic environments are highly variable; to function in them it is necessary to robustly represent important sounds despite changes in acoustic context. Recent studies have shown that neurons in primary auditory cortex (A1) modulate their response gain in an adaptive manner to account for changes in spectro-temporal statistics of different acoustic environments. Based on the results of studies across sensory modalities, we hypothesize that response gain is differentially modulated by two different types of cortical inhibitory neurons: parvalbumin-positive (PV) and somatostatin-positive (SOM) interneurons. Using this circuit-level mechanism, neurons can develop stimulus representations that are invariant to a variety of stimulus contexts by adaptively shifting their response gain. Here, we test whether and how gain adaptation shapes neural and behavioral responses to tones embedded in noise and examine how PV and SOM interneurons mediate this process. First, we examine how PVs and SOMs control gain adaptation by optogenetically manipulating these interneurons while recording neural responses in A1 to alternating low and high contrast noise bursts. We then measure and compute neural response gain at different temporal offsets relative to contrast transitions, and test how optogenetic manipulation of PV or SOM activity controls dynamic shifts in gain across multiple time scales. Next, we train mice to detect target tones at variable temporal delays relative to contrast transitions using a go-nogo paradigm while simultaneously recording neural activity and applying optogenetic inhibition of PVs and SOMs. With these data, we then test whether and how manipulations of neural gain impact neural coding and subsequent behavior. This research will, for the first time, examine the cell-specific cortical mechanisms underlying gain adaptation and their influence on behavioral detection of signals in noise, significantly improving our understanding of sound detection in variable acoustic environments.