The Brain Filters

Our brains have a built-in "noise cancelling" circuit known as a cortical filter that helps us ignore the sounds of our own movements, such as footsteps. This filter is essential for our daily lives, as it allows us to focus on important sounds and conversations without being distracted by the noise of our own actions (or even voice of people we got used to filter out, like mothers or wives). However, in some cases, this filter may not work properly, leading to conditions such as tinnitus, where the brain struggles to filter out certain sounds. For example, a person with tinnitus may hear a constant ringing or buzzing noise, even in a quiet room. 

Cochlear implant recipients may also struggle to adjust to the new sounds they are able to hear after receiving the implant, as their brains may have difficulty filtering out their own movement sounds at first. However, with time and practice, the brain can learn to use the cortical filter more effectively, allowing people to focus on the sounds that matter most to them.

Sounds that arise from our own movements, such as vocalizing or walking, are known as reafferent sounds. These sounds are essential for normal hearing, as they help us to anticipate and distinguish them from sounds that come from the environment. However, the neural circuits that allow us to learn to anticipate and distinguish these sounds are not well understood. 

In order to study these neural circuits, researchers developed a system called acoustic virtual reality (aVR), in which mice were trained to associate a novel sound with their own movements. By using this system, the researchers were able to identify the neural mechanisms that learn to suppress reafferent sounds and study the behavioral consequences of this predictable sensorimotor experience. 

The researchers 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, or ability to change in response to experience, was behaviorally adaptive, as mice that had experienced aVR showed an enhanced ability to detect non-reafferent tones during movement. 

Overall, these findings suggest that there is a dynamic sensory filter in the brain that involves motor cortical inputs to the auditory cortex, and that this filter can be shaped by experience to selectively suppress the predictable acoustic consequences of movement.

REFERENCES

Schneider DM, Sundararajan J, Mooney R. A cortical filter that learns to suppress the acoustic consequences of movement. Nature. 2018 Sep;561(7723):391-5.

Big Data of Sounds

Hearing begins with the ears, but it's more than the sum of sounds. We are able to recognize piano notes and experience music, understand speech in noisy surroundings and localize voice in 3D.  Is it because the sound transmitted to the inner ear is broken down into frequencies and mapped onto the brain like musical notes are mapped on a piano keyboard? But are not we able to hear different things at once in three dimensions? Are not we translating frequencies into meanings on the fly, processing Big Data better than any famous statistician? As Jacob Oppenheim and Marcelo Magnasco showed that simple decomposition of sounds into its components by Fourier transform loses important information about the sound's duration, something that our brain is actually able to overcome. Humans can beat the limits of Fourier analysis, and the Brain processes the big data of sounds better than existing algorithms do. As a matter of fact, training our brain on music makes us better in math and problem solving skills. While inability to process signals in the brain leads to developmental disorders. More to discover, more to learn.

REFERENCES

Oppenheim, Jacob N., and Marcelo O. Magnasco. "Human Time-Frequency Acuity Beats the Fourier Uncertainty Principle." arXiv preprint arXiv:1208.4611(2012).

Díaz, Begoña, et al. "Dysfunction of the auditory thalamus in developmental dyslexia." Proceedings of the National Academy of Sciences 109.34 (2012): 13841-13846.