Fast evaluation of a time-domain non-linear cochlear model on GPUs

Sabo, Doron; Barzelay, Oded; Weiss, Shlomo; Furst, Miriam

doi:10.1016/j.jcp.2014.01.044

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…The above cochlear model was solved in the time domain by implementing a parallel algorithm on a commodity graphics processor unit (GPU) [14].The output of the model is the BM velocity (ξ bm ( x, t ) ) as a response to an acoustic stimulus P in ( t ) . Figure 3 represents the basilar membrane velocity relative to the input level at two points along the cochlear partition.…”

Section: Simulation Results: the Effect Of Outer Hair Cells Lossmentioning

confidence: 99%

“…Modeling the human ear requires a detailed model of the cochlea and the middle and outer ears. A common approach is to model the inner ear as a one-dimensional structure [e.g., 6,14,[20][21][22][23] with the cochlea regarded as an uncoiled structure with two fluid-filled compartments with rigid walls that are separated by an elastic partition, the basilar membrane. The cochlear partition, whose mechanical properties are describable in terms of point-wise mass density, stiffness, and damping, is regarded as a flexible boundary between scala tympani and scala vestibuli.…”

Section: The Human Ear Modelmentioning

confidence: 99%

“…The purpose of this chapter is to introduce a comprehensive, nonlinear time domain cochlear model [6,[12][13][14], followed by a model of the auditory nerve (AN) response [7,13,16,17] that can be used to predict hearing abilities of people with normal cochlea as well as with abnormal cochlea that suffers from either OHC loss and/or IHC loss.…”

Section: Introductionmentioning

confidence: 99%

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Cochlear Model for Hearing Loss

Furst¹

2015

Update on Hearing Loss

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In many psychoacoustical tasks, hearing-impaired subjects display abnormal audiograms and poor understanding of speech compared to normal listeners. Δxisting models that explain the performance of the hearing impaired indicate that possible sources for cochlear hearing loss may be the dysfunction of the outer and inner hair cells. In this study, a model of the auditory system is introduced. It includes two stages a nonlinear time domain cochlear model with active outer hair cells that are driven by the tectorial membrane motion and a synaptic model that generates the auditory nerve instantaneous rate as a response to the basilar membrane motion and is affected by the inner hair cell transduction efficiency. The model can fit both a normal auditory system and an abnormal auditory system with easily induced pathologies.In typical psychoacoustical detection experiments, the ability of subjects to perceive a minimum difference in a physical property is measured. We use the model presented here to predict these performances by assuming that the brain behaves as an optimal processor that estimates a particular physical parameter. The performance of the optimal processor is derived by calculating its lower bound. Since neural activity is described as a nonhomogeneous Poisson process whose instantaneous rate was derived, the CramerRao lower bound can be analytically obtained for both rate coding and all information coding.We compared the model predictions of normal and abnormal cochleae to human thresholds of pure tones in quiet and in the presence of background noise.

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Section: Simulation Results: the Effect Of Outer Hair Cells Lossmentioning

confidence: 99%

Section: The Human Ear Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cochlear Model for Hearing Loss

Furst¹

2015

Update on Hearing Loss

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“…(11) Model presented in Ref. [37] Time domain and resonant characteristics of the cochlea are obtained by solving a set of nonlinear partial differential equations.…”

Section: Contemporary Models Of the Cochlear Amplifiermentioning

confidence: 99%

Power Amplification and Frequency Selectivity in the Inner Ear: A New Physical Model

Kiełczyński¹

2017

Advances in Clinical Audiology

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This Chapter presents a new physical model for signal processing phenomena (power amplification and frequency selectivity) occurring in the inner ear (Cochlea). It is generally accepted that Outer Hair Cells (OHCs) play a pivotal role in the Cochlear signal processing. In the proposed new model we postulate that all signal processing phenomena in the Cochlea are due to electrical currents flowing in the Cochlea structure. Three crucial characteristics of the OHCs are: 1) a forward mechanoelectrical transduction, 2) a strong piezoelectric effect (direct and inverse), and 3) a transmembrane nonlinear capacitance. The new model postulates existence of a biological electromechanical transistor (EMT) in each of the OHCs (based on a forward mechanoelectrical transduction phenomenon), which enhances the power of an incoming acoustic signal. Consequently, the nonlinear capacitance of the appropriate OHCs is charged (pumped) by an AC current source generated at the output of the proposed EMT transistor. Power amplification and frequency selectivity are realized on the nonlinear capacitance, which constitutes an essential part of a parametric amplifier circuit. The amplified and sharpened in frequency electric signal is then converted to a mechanical signal by the OHCs (inverse piezoelectric effect) and transferred to the Inner Hair Cells that transform this mechanical signal into an output electrical signal supplied to the afferent nerves.

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“…Historically, GPUs are electronic components used in computer graphics hardware. They are getting more widely used to accelerate computations in many elds (Mametjanov et al, 2012;Cekmez et al, 2013;Sabo et al, 2014). Due to its architecture, the GPUs solution is advantageous for problems for which a parallel execution is possible (Goldsworthy, 2014).…”

Section: Introductionmentioning

confidence: 99%

Accelerating viability kernel computation with CUDA architecture: application to bycatch fishery management

Brias¹,

Mathias²,

Deffuant³

2016

Comput Manag Sci

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Computing a viability kernel consumes time and memory resources which increase exponentially with the dimension of the problem. This curse of dimensionality strongly limits the applicability of this approach, otherwise promising. We report here an attempt to tackle this problem with Graphics Processing Units (GPU). We design and implement a version of the viability kernel algorithm suitable for General Purpose GPU (GPGPU) computing using Nvidia's architecture, CUDA (Computing Unied Device Architecture). Dierent parts of the algorithm are parallelized on the GPU device and we test the algorithm on a dynamical system of theoretical population growth. We study computing time gains as a function of the number of dimensions and the accuracy of the grid covering the state space. The speed factor reaches up to 20 with the GPU version compared to the Central Processing Unit (CPU) version, making the approach more applicable to problems in 4 to 7 dimen

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Fast evaluation of a time-domain non-linear cochlear model on GPUs

Cited by 7 publications

References 12 publications

Cochlear Model for Hearing Loss

Cochlear Model for Hearing Loss

Power Amplification and Frequency Selectivity in the Inner Ear: A New Physical Model

Accelerating viability kernel computation with CUDA architecture: application to bycatch fishery management

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