Modeling Sound Localization with Cochlear Implants

Nicoletti, Michele; Wirtz, C.; Hemmert, Werner

doi:10.1007/978-3-642-37762-4_12

Cited by 9 publications

(11 citation statements)

References 73 publications

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“…Between these two ends of the spectrum, single-compartment (point neuron) models with various internal dynamics have been created, such as simple comparison [ 46 ] or temporal summation [ 47 ] of excitatory and inhibitory inputs, an electric circuit model of resonating membrane potentials [ 48 ], a leaky integrate-and-fire model with standard configurations [ 49 ], with synaptic plasticity [ 50 ] or with afterhyperpolarization [ 51 ], and a Hodgkin-Huxley (HH)-type conductance-based model with several types of ion channels [ 52 , 53 ]; see [ 54 ] for a more detailed review of earlier modeling approaches. Recently, LSO models (either abstract or physiology-based) have been incorporated with larger scale simulations, for example, to study psychophysical outcomes [ 55 , 56 ], to develop bio-inspired neural networks of sound localization [ 57 ], and to evaluate binaural hearing of cochlear implant users [ 58 , 59 ].…”

Section: Introductionmentioning

confidence: 99%

Physiological models of the lateral superior olive

2017

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In computational biology, modeling is a fundamental tool for formulating, analyzing and predicting complex phenomena. Most neuron models, however, are designed to reproduce certain small sets of empirical data. Hence their outcome is usually not compatible or comparable with other models or datasets, making it unclear how widely applicable such models are. In this study, we investigate these aspects of modeling, namely credibility and generalizability, with a specific focus on auditory neurons involved in the localization of sound sources. The primary cues for binaural sound localization are comprised of interaural time and level differences (ITD/ILD), which are the timing and intensity differences of the sound waves arriving at the two ears. The lateral superior olive (LSO) in the auditory brainstem is one of the locations where such acoustic information is first computed. An LSO neuron receives temporally structured excitatory and inhibitory synaptic inputs that are driven by ipsi- and contralateral sound stimuli, respectively, and changes its spike rate according to binaural acoustic differences. Here we examine seven contemporary models of LSO neurons with different levels of biophysical complexity, from predominantly functional ones (‘shot-noise’ models) to those with more detailed physiological components (variations of integrate-and-fire and Hodgkin-Huxley-type). These models, calibrated to reproduce known monaural and binaural characteristics of LSO, generate largely similar results to each other in simulating ITD and ILD coding. Our comparisons of physiological detail, computational efficiency, predictive performances, and further expandability of the models demonstrate (1) that the simplistic, functional LSO models are suitable for applications where low computational costs and mathematical transparency are needed, (2) that more complex models with detailed membrane potential dynamics are necessary for simulation studies where sub-neuronal nonlinear processes play important roles, and (3) that, for general purposes, intermediate models might be a reasonable compromise between simplicity and biological plausibility.

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Section: Introductionmentioning

confidence: 99%

Physiological models of the lateral superior olive

2017

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“…This approach is straight-forward in Hodgkin-Huxley type models (Hodgkin and Huxley 1952;Abilez et al 2011;Rattay et al 2001), whereas the integration of light-induced currents is less clear in phenomenological models (Bruce et al 1999a, b). Depending on the research question, models with the appropriate complexity can be chosen, like point neuron models (Nicoletti et al 2013), multicompartment models (McNeal 1976), or full three-dimensional models.…”

Section: Modeling Neuronal Responses In Transfected Neuronsmentioning

confidence: 99%

“…Here we combined an auditory nerve model proposed by Nicoletti et al (2013) and Rudnicki et al (2015) and gained responsiveness to light by including a modeled ChR2-channel. Dynamic simulations were conducted with BRIAN (http://www.briansimulator.org) (Goodman and Brette 2009).…”

Section: Modeling Neuronal Responses In Transfected Neuronsmentioning

confidence: 99%

Optogenetic stimulation of the cochlea—A review of mechanisms, measurements, and first models

Weiss

Voss

Hemmert

2016

Network: Computation in Neural Systems

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This review evaluates the potential of optogenetic methods for the stimulation of the auditory nerve and assesses the feasability of optogenetic cochlear implants (CIs). It provides an overview of all critical steps like opsin targeting strategies, how opsins work, how their function can be modeled and included in neuronal models and the properties of light sources available for optical stimulation. From these foundations, quantitative estimates for the number of independent stimulation channels and the temporal precision of optogenetic stimulation of the auditory nerve are derived and compared with state-of-the-art electrical CIs. We conclude that optogenetic CIs have the potential to increase the number of independent stimulation channels by up to one order of magnitude to about 100, but only if light sources are able to deliver confined illumination patterns independently and parallelly. Already now, opsin variants like ChETA and Chronos enable driving of the auditory nerve up to rates of 200 spikes/s, close to the physiological value of their maximum sustained firing rate. Apart from requiring 10 times more energy than electrical stimulation, optical CIs still face major hurdles concerning the safety of gene transfection and optrode array implantation, for example, before becoming an option to replace electrical CIs.

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“…At its most basic level, neural excitation may be probed using the activating function (Rattay 1986) which is the second spatial derivative of the potential field on a plane that intersects the nodes of Ranvier. Examples of where this approach was employed are the models of Finley et al (1990), Rattay et al (2001a) and Nicoletti et al (2013). The activating function assumes that the neuron is in the resting state and only indicates the direction and degree to which the node is inclined to change from the resting state.…”

Section: Neural Modelsmentioning

confidence: 99%

Three-dimensional models of cochlear implants: A review of their development and how they could support management and maintenance of cochlear implant performance

Hanekom

2016

Network: Computation in Neural Systems

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Three-dimensional (3D) computational modeling of the auditory periphery forms an integral part of modern-day research in cochlear implants (CIs). These models consist of a volume conduction description of implanted stimulation electrodes and the current distribution around these, coupled with auditory nerve fiber models. Cochlear neural activation patterns can then be predicted for a given input stimulus. The objective of this article is to present the context of 3D modeling within the field of CIs, the different models, and approaches to models that have been developed over the years, as well as the applications and potential applications of these models. The process of development of 3D models is discussed, and the article places specific emphasis on the complementary roles of generic models and user-specific models, as the latter is important for translation of these models into clinical application.

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Modeling Sound Localization with Cochlear Implants

Cited by 9 publications

References 73 publications

Physiological models of the lateral superior olive

Physiological models of the lateral superior olive

Optogenetic stimulation of the cochlea—A review of mechanisms, measurements, and first models

Three-dimensional models of cochlear implants: A review of their development and how they could support management and maintenance of cochlear implant performance

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