When sound waves enter the inner ear, specialized neurons detect the vibrations and transmit crucial signals to the brain. These signals encompass a wealth of information that empowers us to engage in conversations, recognize familiar voices, appreciate music, and swiftly locate a ringing phone or a crying baby.
Neurons communicate by generating spikes—brief fluctuations in voltage that travel along nerve fibers, termed action potentials. Remarkably, auditory neurons can discharge hundreds of spikes per second, rhythmically aligning their spikes with the oscillations of incoming sound waves. This exact timing is essential for deciphering auditory information, including voice recognition and sound localization, as discovered by scientists at MIT’s McGovern Institute for Brain Research.
The groundbreaking study, published on Dec. 4 in Nature Communications, demonstrates how machine learning enhances our understanding of how the brain interprets auditory information in real-world contexts. MIT professor and McGovern investigator Josh McDermott, who spearheaded the research, emphasizes that these models equip scientists to better explore the effects of various hearing impairments and develop more effective interventions.
The Science of Sound
The auditory signals in our nervous system are timed with incredible accuracy. Researchers have long thought that this timing is crucial to our perception of sound. Sound waves oscillate at different rates, determining their pitch: slow oscillations for low-pitched sounds and quicker oscillations for high-pitched sounds. The auditory nerve, which carries information from sound-detecting hair cells in the ear to the brain, fires electrical spikes corresponding to these oscillation frequencies. “The action potentials in an auditory nerve fire at very specific times in relation to the peaks in the stimulus waveform,” explains McDermott, an associate head of the MIT Department of Brain and Cognitive Sciences.
This phenomenon, known as phase-locking, necessitates that neurons synchronize their spikes with sub-millisecond precision. Despite this, scientists have struggled to grasp how these temporal patterns influence brain function. This inquiry has significant clinical ramifications: “If we aim to design a prosthetic device that provides electrical signals to the brain similar to natural ear function, understanding what information matters in a healthy ear is critical,” McDermott states.
Studying this has proved challenging; animal models offer limited insights into how the human brain decodes language or music, and direct study of the auditory nerve in humans is impractical. Consequently, McDermott and graduate student Mark Saddler, PhD ’24, turned to artificial neural networks.
Artificial Hearing
Neuroscientists have utilized computational models for years to investigate how the brain may decode sensory information. However, previous models were often constrained to oversimplified tasks due to computational limitations. “One issue with earlier models is that they often perform too well,” says Saddler, now at the Technical University of Denmark. For instance, a computational model tasked with identifying a higher pitch in a pair of simple tones often surpasses human performance—a scenario that doesn’t reflect everyday hearing tasks. “The brain does not optimize for such artificial tasks,” he notes, restricting the insights obtainable from earlier models.
To gain deeper insights into human hearing, Saddler and McDermott crafted a hearing model capable of performing tasks the way people typically use their hearing, such as recognizing words and identifying voices. This involved developing an artificial neural network that mimics the brain’s auditory processing areas, using input from approximately 32,000 simulated sound-detecting sensory neurons and optimizing it for real-world tasks.
The results showed that their model closely mirrored human auditory behavior—outperforming previous models, according to McDermott. In one examination, the artificial neural network was tasked with recognizing words and voices amid various background noises, from the drone of an airplane to the sound of applause. Across all scenarios, the model’s performance aligned closely with human capabilities.
However, when the timing of the spikes in the simulated ear was disrupted, the model struggled to replicate human performance in recognizing voices or pinpointing sound locations. McDermott’s previous work indicated that humans utilize pitch for voice recognition, yet the model revealed that this ability diminishes without precisely timed signals. “To account for human behavior and excel at the task, precise spike timing is essential,” Saddler explains. This finding underscores that the brain relies on exact auditory signals for practical hearing experiences.
The team’s discoveries illustrate how artificial neural networks can enhance neuroscientists’ understanding of how auditory information influences our world perception, whether hearing is intact or compromised. “Linking auditory nerve firing patterns to behavior opens numerous possibilities,” McDermott remarks.
“With these models connecting ear neural responses to auditory behavior, we can explore the implications of different hearing loss types on our auditory abilities,” he adds. “This knowledge will improve our diagnosis of hearing loss and may lead to better design for hearing aids or cochlear implants.” For instance, individuals often face various limitations with cochlear implants—what configurations would enable optimal mediation of auditory behaviors? These models could provide critical insights to answer such questions.
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Photo credit & article inspired by: Massachusetts Institute of Technology