My broad research goal is to understand how the auditory cortex processes complex sounds such as speech and other natural sounds. Because of my focus on speech and higher order processing, my research uses human rather than animal subjects. To non-invasively record and analyze real-time neural processing in humans, I use magnetoencephalography (MEG), because of its high temporal resolution (milliseconds), reasonable spatial resolution (millimeters), and silent operation. My primary research topics address the question of how the brain turns sound into hearing (surprisingly, the objective sounds impinging upon our ears are not very tightly linked to what we hear). Neural computations employ algorithms developed and fine-tuned by millions of years of evolution. As such the computations are typically far beyond the capability of even the most advanced computers. But by identifying, understanding, and quantitatively characterizing the computations performed by the brain, it is possible to determine those algorithms. This computational level of understanding has great potential benefits to engineering applications (e.g. auditory-based identification methods, robust speaker identification, robust speech processing) as well as to health-related applications (hearing aids and cochlear implants that would actually function well in a noisy environment). The class of neural computations that use the temporal character of the sounds being processed—those for which time plays an important role—are the primary focus of my research. This includes the neural computations employed in the processing of rhythmic sounds, e.g., speech or simple repeating patterns, and in the disentanglement of an individual rhythmic sound from other competing sounds.

Research Interests: 

• Investigations of how the brain solves the “Cocktail Party” problem, i.e., how, in a crowded and noisy environment, we have the ability to hone in on a single auditory source (e.g. one person talking), while simultaneously suppressing all the remaining interfering sounds
• How the brain represents complex sounds such as human speech
• How the brain’s representations of complex sounds are built up from representations of much simpler building blocks (acoustic modulations)
• Advances in neural signal processing

For more information, please visit Dr. Simon’s lab website: https://cansl.umd.edu/