Neural circuits underlying auditory contrast gain control and their perceptual implications

Abstract

Neural adaptation enables sensory information to be represented optimally in the brain despite large fluctuations over time in the statistics of the environment. Auditory contrast gain control represents an important example, which is thought to arise primarily from cortical processing. Here we show that neurons in the auditory thalamus and midbrain of mice show robust contrast gain control, and that this is implemented independently of cortical activity. Although neurons at each level exhibit contrast gain control to similar degrees, adaptation time constants become longer at later stages of the processing hierarchy, resulting in progressively more stable representations. We also show that auditory discrimination thresholds in human listeners compensate for changes in contrast, and that the strength of this perceptual adaptation can be predicted from physiological measurements. Contrast adaptation is therefore a robust property of both the subcortical and cortical auditory system and accounts for the short-term adaptability of perceptual judgments.

Contrast gain control occurs independently of both parvalbumin-positive interneuron activity and shunting inhibition in auditory cortex

“…Short-term synaptic depression at the thalamocortical synapse is believed to contribute to contrast gain control in V1 and may also do so in auditory cortex (Banitt et al 2007;Carandini et al 2002). Recent evidence has demonstrated that contrast gain control is also exhibited by neurons in the mouse inferior colliculus and medial geniculate body, indicating that this is not an emergent property of auditory cortex (Lohse et al 2020). Nevertheless, the time constants of contrast adaptation are longer in the auditory cortex than at subcortical levels (Lohse et al 2020;Rabinowitz et al 2013), so it is likely that local processing contributes to these computations at each processing level.…”

“…Recent evidence has demonstrated that contrast gain control is also exhibited by neurons in the mouse inferior colliculus and medial geniculate body, indicating that this is not an emergent property of auditory cortex (Lohse et al 2020). Nevertheless, the time constants of contrast adaptation are longer in the auditory cortex than at subcortical levels (Lohse et al 2020;Rabinowitz et al 2013), so it is likely that local processing contributes to these computations at each processing level. Further characterization of the circuits and mechanisms underlying canonical computations such as contrast gain control across sensory modalities holds the promise of not only providing insight into the specific system being studied but also into fundamental questions regarding the relationship between mechanistic and computational levels of understanding in neuroscience.…”

“…We performed all extracellular and intracellular recordings from the auditory cortex of anesthetized mice. The anesthetized preparation was deemed appropriate for these experiments as contrast gain control in the auditory cortex (Rabinowitz et al 2011) and midbrain (Lohse et al 2020) has been found to be unaffected by anesthesia. Similarly, research into the role of PV interneuron activity is routinely investigated in both awake (e.g., Natan et al 2017) and anesthetized (e.g., Atallah et al 2012) preparations, and no difference between these conditions was reported in experiments in which the effects of optogenetic activation of PV interneurons on activity in auditory cortex was examined (Blackwell et al 2020).…”

“…This helps to create sensory representations that are invariant to factors such as changes in illumination or background noise. Contrast adaptation has also been shown to affect perceptual thresholds (Greenlee and Heitger 1988;Lohse et al 2020).…”

A Survey on Probabilistic Models in Human Perception and Machines

“…However, standard STRF and LN models do not incorporate the highly nonlinear and dynamic neural processes which are important for noise robustness (for reviews, see Meyer et al, 2017 ; King et al, 2018 ). For example, auditory neurons adapt to stimulus statistics, such as the mean level and the contrast (i.e., the sound level variance) of recent sounds, and adjust their sensitivity accordingly; this adaptation enables efficient and robust neural coding (Fritz et al, 2003 ; David et al, 2012 ; Rabinowitz et al, 2013 ; Willmore et al, 2014 , 2016 ; Lohse et al, 2020 ). STRF models extended with adaptive kernels (Rabinowitz et al, 2012 ) and other nonlinear features, such as input nonlinearity (Ahrens et al, 2008 ), synaptic depression (Mesgarani et al, 2014 ), gain normalization (Mesgarani et al, 2014 ), or top-down influence, such as feedback (Calabrese et al, 2011 ) and selective attention (Mesgarani and Chang, 2012 ), have been shown to better account for noise robustness.…”