Introduction: Computational modeling of cochlear implant stimulation has a long history, but its development has mostly been restricted to generic models, with patient-specific modeling being relatively rare, in spite of its potential applications in both research and clinical practice. Areas covered: The present state of computational cochlear implant models is discussed in relation to patient-specific modeling. From three-dimensional geometries derived from clinical imaging to full end-to-end models of the electrically stimulated peripheral auditory system, computational cochlear implant models have progressed to the point where they can meaningfully simulate responses to complex (speech) stimuli. Expert opinion: The development of patient-specific models that could be used to study the underlying mechanisms of cochlear implant functioning and ultimately be applied to make clinical diagnoses and recommendations, is within reach. However, there are still obstacles to overcome; the most immediate of these is the issue of auditory neural health, which is currently impossible to definitively assess in a living subject, yet has profound effects on electrical stimulation.

Many speech coding strategies have been developed over the years, but comparing them has been convoluted due to the difficulty in disentangling brand-specific and patient-specific factors from strategy-specific factors that contribute to speech understanding. Here, we present a comparison with a ‘virtual’ patient, by comparing two strategies from two different manufacturers, Advanced Combination Encoder (ACE) versus HiResolution Fidelity 120 (F120), running on two different implant systems in a computational model with the same anatomy and neural properties. We fitted both strategies to an expected T-level and C- or M-level based on the spike rate for each electrode contact’s allocated frequency (center electrode frequency) of the respective array. This paper highlights neural and electrical differences due to brand-specific characteristics such as pulse rate/channel, recruitment of adjacent electrodes, and presence of subthreshold pulses or interphase gaps. These differences lead to considerably different recruitment patterns of nerve fibers, while achieving the same total spike rates, i.e., loudness percepts. Also, loudness growth curves differ significantly between brands. The model is able to demonstrate considerable electrical and neural differences in the way loudness growth is achieved in CIs from different manufacturers.