A computational modelling framework for assessing information transmission with cochlear implants.

Computational models are useful tools to investigate scientific questions that would be complicated to address using an experimental approach. In the context of cochlear-implants (CIs), being able to simulate the neural activity evoked by these devices could help in understanding their limitations to provide natural hearing. Here, we present a computational modelling framework to quantify the transmission of information from sound to spikes in the auditory nerve of a CI user. The framework includes a model to simulate the electrical current waveform sensed by each auditory nerve fiber (electrode-neuron interface), followed by a model to simulate the timing at which a nerve fiber spikes in response to a current waveform (auditory nerve fiber model). Information theory is then applied to determine the amount of information transmitted from a suitable reference signal (e.g., the acoustic stimulus) to a simulated population of auditory nerve fibers. As a use case example, the framework is applied to simulate published data on modulation detection by CI users obtained using direct stimulation via a single electrode. Current spread as well as the number of fibers were varied independently to illustrate the framework capabilities. Simulations reasonably matched experimental data and suggested that the encoded modulation information is proportional to the total neural response. They also suggested that amplitude modulation is well encoded in the auditory nerve for modulation rates up to 1000 Hz and that the variability in modulation sensitivity across CI users is partly because different CI users use different references for detecting modulation.

Optimization of Sound Coding Strategies to Make Singing Music More Accessible for Cochlear Implant Users.

Cochlear implants (CIs) are implantable medical devices that can partially restore hearing to people suffering from profound sensorineural hearing loss. While these devices provide good speech understanding in quiet, many CI users face difficulties when listening to music. Reasons include poor spatial specificity of electric stimulation, limited transmission of spectral and temporal fine structure of acoustic signals, and restrictions in the dynamic range that can be conveyed via electric stimulation of the auditory nerve. The coding strategies currently used in CIs are typically designed for speech rather than music. This work investigates the optimization of CI coding strategies to make singing music more accessible to CI users. The aim is to reduce the spectral complexity of music by selecting fewer bands for stimulation, attenuating the background instruments by strengthening a noise reduction algorithm, and optimizing the electric dynamic range through a back-end compressor. The optimizations were evaluated through both objective and perceptual measures of speech understanding and melody identification of singing voice with and without background instruments, as well as music appreciation questionnaires. Consistent with the objective measures, results gathered from the perceptual evaluations indicated that reducing the number of selected bands and optimizing the electric dynamic range significantly improved speech understanding in music. Moreover, results obtained from questionnaires show that the new music back-end compressor significantly improved music enjoyment. These results have potential as a new CI program for improved singing music perception.

Modeling temporal information encoding by the population of fibers in the healthy and synaptopathic auditory nerve.

We report a theoretical study aimed at investigating the impact of cochlear synapse loss (synaptopathy) on the encoding of the envelope (ENV) and temporal fine structure (TFS) of sounds by the population of auditory nerve fibers. A computational model was used to simulate auditory-nerve spike trains evoked by sinusoidally amplitude-modulated (AM) tones at 10 Hz with various carrier frequencies and levels. The model included 16 cochlear channels with characteristic frequencies (CFs) from 250 Hz to 8 kHz. Each channel was innervated by 3, 4 and 10 fibers with low (LSR), medium (MSR), and high spontaneous rates (HSR), respectively. For each channel, spike trains were collapsed into three separate 'population' post-stimulus time histograms (PSTHs), one per fiber type. Information theory was applied to reconstruct the stimulus waveform, ENV, and TFS from one or more PSTHs in a mathematically optimal way. The quality of the reconstruction was regarded as an estimate of the information present in the used PSTHs. Various synaptopathy scenarios were simulated by removing fibers of specific types and/or cochlear regions before stimulus reconstruction. We found that the TFS was predominantly encoded by HSR fibers at all stimulus carrier frequencies and levels. The encoding of the ENV was more complex. At lower levels, the ENV was predominantly encoded by HSR fibers with CFs near the stimulus carrier frequency. At higher levels, the ENV was equally well or better encoded by HSR fibers with CFs different from the AM carrier frequency as by LSR fibers with CFs at the carrier frequency. Altogether, findings suggest that a healthy population of HSR fibers (i.e., including fibers with CFs around and remote from the AM carrier frequency) might be sufficient to encode the ENV and TFS over a wide range of stimulus levels. Findings are discussed regarding their relevance for diagnosing synaptopathy using non-invasive ENV- and TFS-based measures.