A cortical filter that learns to suppress the acoustic consequences of movement

Abstract

Sounds can arise from the environment and also predictably from many of our own movements, such as vocalizing, walking, or playing music. The capacity to anticipate these movement-related (reafferent) sounds and distinguish them from environmental sounds is essential for normal hearing, but the neural circuits that learn to anticipate the often arbitrary and changeable sounds that result from our movements remain largely unknown. Here we developed an acoustic virtual reality (aVR) system in which a mouse learned to associate a novel sound with its locomotor movements, allowing us to identify the neural circuit mechanisms that learn to suppress reafferent sounds and to probe the behavioural consequences of this predictable sensorimotor experience. We found that aVR experience gradually and selectively suppressed auditory cortical responses to the reafferent frequency, in part by strengthening motor cortical activation of auditory cortical inhibitory neurons that respond to the reafferent tone. This plasticity is behaviourally adaptive, as aVR-experienced mice showed an enhanced ability to detect non-reafferent tones during movement. Together, these findings describe a dynamic sensory filter that involves motor cortical inputs to the auditory cortex that can be shaped by experience to selectively suppress the predictable acoustic consequences of movement.

Corollary Discharge versus Efference Copy: Distinct Neural Signals in Speech Preparation Differentially Modulate Auditory Responses

“…2. These behavior and EEG results were consistent with immense literature about action-induced sensory suppression in both animal models (Crapse & Sommer, 2008;Eliades & Wang, 2008;Poulet & Hedwig, 2006;Schneider et al, 2018;Straka et al, 2018) and humans (Blakemore et al, 1998;Houde et al, 2002). Our results suggest that CD provides a uniform inhibitive function that suppresses sensory processing during the action.…”

“…Based on the inhibitory functions, various cognitive abilities and behaviors can be achieved, such as efficient motor control (Kawato, 1999;Miall & Wolpert, 1996;Wolpert & Ghahramani, 2000), stable visual perception (Ross et al, 2001;Sommer & Wurtz, 2006), fluent vocal and speech production and control (Guenther, 1995;Hickok, 2012;John F Houde & Nagarajan, 2011;Tian, 2010), self-monitoring and agency (Sarah-Jayne Blakemore & Decety, 2001;Desmurget et al, 2009;Grush, 2004). Such motor-to-sensory transformation mechanisms have been evident among animal species (Crapse & Sommer, 2008) and their neural pathways have been increasingly mapped out (Poulet & Hedwig, 2006;Schneider et al, 2014Schneider et al, , 2018.…”

Action enhances predicted touch

“…For example, attenuating internally-generated electric fields in Mormyrid fish improves detection of prey-like stimuli, a finding thought to be a consequence of predicting self-generated sensory input (Bell, 2001;Enikolopov et al, 2018). Similarly, virtual reality trained mice show suppressed auditory responses to self-produced tones generated by treadmill running (Schneider et al, 2018) or licking behaviours (Singla et al, 2017), compared to no movement. In humans, studies measuring the perceived force of a tactile stimulus during movement show similar attenuation effects during movement relative to no movement, and commonly attribute these effects to predictive mechanisms (Bays et al, 2005(Bays et al, , 2006.…”

Spectral cues are necessary to encode azimuthal auditory space in the mouse superior colliculus

“…First, the mouse was alert and freely running on a cylindrical treadmill during the neural recording session. Locomotion modulates the functional properties of the visually responsive neurons 31,33,34 and the auditory cortical neurons 35,36 . To determine the effects of locomotion on the auditory SC neurons, we separated the session into segments when the mouse was running (speed >1 cm/s) and when it was stationary.…”