The model should conduct the experiment because it knows best which condition is going to be the most informative for confining its free parameters, at least in theory
The computational infrastructure and software needed to implement computational audiology
A cloud-based web app provides an accessible tool for simulation and visualization of population responses of model auditory-nerve and midbrain neurons.
The biggest hurdle facing computational audiologists may not be perfecting machine learning methods, but creating the infrastructure- including hardware, data management tools, and software engineering skills- to support this new type of research.
Applying biophysical auditory periphery models for real-time applications and studies of hearing impairment
The rise of new digital tools for collecting data on scales never before seen in our field coupled with new modeling techniques from deep learning requires us to think about what computational infrastructure we need in order to fully enjoy the benefits and mitigate associated barriers.
Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.