The history and future of neural modeling for cochlear implants

Seeber, Bernhard U.; Bruce, Ian C.

doi:10.1080/0954898x.2016.1223365

Cited by 11 publications

(7 citation statements)

References 75 publications

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“…However, it is not presently known the extent that modeling traveling wave mechanics is important for conveying temporal fine structure cues (Loeb et al, 1983;Loeb, 2005;McGinley et al, 2012). Computational modeling can be used to clarify the tradeoffs between emulating traveling wave mechanics and minimizing the smearing of temporal cues for pitch perception (Cohen, 2009;Karg et al, 2013;Seeber and Bruce, 2016;van Gendt et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Computational Modeling of Synchrony in the Auditory Nerve in Response to Acoustic and Electric Stimulation

Goldsworthy

2022

Front. Comput. Neurosci.

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Cochlear implants are medical devices that provide hearing to nearly one million people around the world. Outcomes are impressive with most recipients learning to understand speech through this new way of hearing. Music perception and speech reception in noise, however, are notably poor. These aspects of hearing critically depend on sensitivity to pitch, whether the musical pitch of an instrument or the vocal pitch of speech. The present article examines cues for pitch perception in the auditory nerve based on computational models. Modeled neural synchrony for pure and complex tones is examined for three different electric stimulation strategies including Continuous Interleaved Sampling (CIS), High-Fidelity CIS (HDCIS), and Peak-Derived Timing (PDT). Computational modeling of current spread and neuronal response are used to predict neural activity to electric and acoustic stimulation. It is shown that CIS does not provide neural synchrony to the frequency of pure tones nor to the fundamental component of complex tones. The newer HDCIS and PDT strategies restore synchrony to both the frequency of pure tones and to the fundamental component of complex tones. Current spread reduces spatial specificity of excitation as well as the temporal fidelity of neural synchrony, but modeled neural excitation restores precision of these cues. Overall, modeled neural excitation to electric stimulation that incorporates temporal fine structure (e.g., HDCIS and PDT) indicates neural synchrony comparable to that provided by acoustic stimulation. Discussion considers the importance of stimulation rate and long-term rehabilitation to provide temporal cues for pitch perception.

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Section: Discussionmentioning

confidence: 99%

Computational Modeling of Synchrony in the Auditory Nerve in Response to Acoustic and Electric Stimulation

Goldsworthy

2022

Front. Comput. Neurosci.

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“…The AF patterns around the cathodic electrodes appeared to be very similar regardless of the electrode configuration (Figure 7, bottom row), suggesting minimal interaction from the anodic contacts. Interference between the contacts can increase with larger separations between the basilar membrane and the electrode carrier in the scala tympani (Frijns et al, 1996(Frijns et al, , 2001Hanekom, 2001;Briaire and Frijns, 2006;Seeber and Bruce, 2016). The additional AF peaks that emerged for the anodic electrodes in the BP and DB configurations were much weaker in the case of TP.…”

Section: The Af Patternsmentioning

confidence: 99%

Improving the stimulation selectivity in the human cochlea by strategic selection of the current return electrode

Cakmak,

Pal,

Sahin

2023

Front. Audiol. Otol.

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The hearing quality provided by cochlear implants is poorly predicted by computer simulations. A high-resolution, human-specific cochlear anatomy is crucial for the accuracy of predictions. In this study, the standard multipolar stimulation paradigms are revisited and Rattay's Activating Function is evaluated in a finite element model of a realistic cochlear geometry that is based on μ-CT images and a commercial lead. The stimulation thresholds across the cochlear fibers were investigated for monopolar, bipolar, tripolar, and a novel (distant) bipolar electrode configuration using an active compartmental nerve model based on Schwartz-Eikhof-Frijns membrane dynamics. The results suggest that jumping of the stimulation point from the vicinity of the cathodic electrode to distant fibers, especially to the low frequency (apical) region of the basilar membrane that is most critical to hearing, occurs more often with monopolar stimulation than other electrode configurations. Bipolar and tripolar electrodes near the apical region did not provide a large threshold margin either. On the other hand, the threshold margin could be improved by proper selection of the electrode for the return current with bipolar stimulation, a technique named here as distant bipolar. The results also demonstrate the significance of having a realistic cochlear geometry in computer models for accurate interpretation for multipolar stimulation paradigms. More selective and focal stimulation may be possible by designing the electrode carrier shape and positioning of the current return electrodes more strategically. This is needed particularly in the apical turn of the cochlea where the current stimulation methods are the least selective.

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“…Motor neuroprosthesis has a long history with intensive studies, in particular with the recent techniques where cortical neuronal spikes can be well recorded and used to control neuroprosthesis [6]. In term of sensory neuroprosthesis, cochlear implants are severed as the most widely used neuroprosthesis, and have a fair good performance for helping hearing loss, although there are many remaining questions about how to improve its performance in a noisy environment and its effect on neuronal activity of downstream auditory cortex [11,12]. However, in contrast to the intensive computational modeling of cochlear implants [11], retinal neuroprosthesis is much less well studied, and has a much worse performance for restoring the eyesight, although a few types of retinal neuroprosthesis are being used in clinical trials [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…As the brain is the central hub to control and exchange information used by our motor, sensory and cognitive behavior, the performance of neuroprosthesis has to rely on how to better analyze the neuronal signal used by neuroprosthesis. Therefore, besides the development of neuroprosthesis hardware, better algorithms are the core feature of neuroprosthesis for better performance [6,10,11].…”

Section: Introductionmentioning

confidence: 99%

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

Liu

Jia

et al. 2020

Engineering

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Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling some part of our brain/body. In terms of vision, incoming information can be processed by the brain in millisecond interval. The retina computes visual scenes and then sends its output as neuronal spikes to the cortex for further computation. Therefore, the neuronal signal of interest for retinal neuroprosthesis is spike. Closed-loop computation in neuroprosthesis includes two stages: encoding stimulus to neuronal signal, and decoding it into stimulus. Here we review some of the recent progress about visual computation models that use spikes for analyzing natural scenes, including static images and dynamic movies. We hypothesize that for a better understanding of computational principles in the retina, one needs a hypercircuit view of the retina, in which different functional network motifs revealed in the cortex neuronal network should be taken into consideration for the retina. Different building blocks of the retina, including a diversity of cell types and synaptic connections, either chemical synapses or electrical synapses (gap junctions), make the retina an ideal neuronal network to adapt the computational techniques developed in artificial intelligence for modeling of encoding/decoding visual scenes. Altogether, one needs a systems approach of visual computation with spikes to advance the next generation of retinal neuroprosthesis as an artificial visual system.

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The history and future of neural modeling for cochlear implants

Cited by 11 publications

References 75 publications

Computational Modeling of Synchrony in the Auditory Nerve in Response to Acoustic and Electric Stimulation

Computational Modeling of Synchrony in the Auditory Nerve in Response to Acoustic and Electric Stimulation

Improving the stimulation selectivity in the human cochlea by strategic selection of the current return electrode

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

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