Analog electronic cochlea with mammalian hearing characteristics

Martignoli, Stefan; Vyver, J.-J. van der; Kern, A.; Uwate, Yoko; Stoop, Ruedi

doi:10.1063/1.2768204

Cited by 46 publications

(38 citation statements)

References 15 publications

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“…This model reproduces virtually all mesoscopic biophysical cochlear observations ( [16][17][18][19][20]; in particular Supplemental Material of Refs. [16,18,19]).…”

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Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

Stoop

Gomez

2016

Phys. Rev. Lett.

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The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

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“…This model reproduces virtually all mesoscopic biophysical cochlear observations ( [16][17][18][19][20]; in particular Supplemental Material of Refs. [16,18,19]).…”

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confidence: 99%

“…Based on the detailed biophysics and nonlinear dynamics at work in the cochlea [13,14], we developed a model of the sensor consisting of such sections [16][17][18]. This model reproduces virtually all mesoscopic biophysical cochlear observations ( [16][17][18][19][20]; in particular Supplemental Material of Refs.…”

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confidence: 99%

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

Stoop

Gomez

2016

Phys. Rev. Lett.

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“…where zðtÞ is the complex-valued variable, μ is the distance to the bifurcation point, and ω ch is the characteristic (angular) frequency of the system [20]. In this formulation, a Hopf bifurcation occurs when the parameter μ changes from negative to positive values: For μ > 0, the system starts to spontaneously oscillate at a frequency ω ch .…”

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“…In the cochlea, single outer hair cells as well as mesoscopic cochlea elements of hundreds of hair cells [10,20] are reliably described by a Hopf system. In this Letter, we elucidate how a single element and ensembles composed of these elements adhere to the same physical description, and what this entrains further.…”

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confidence: 99%

Signal-Coupled Subthreshold Hopf-Type Systems Show a Sharpened Collective Response

2016

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Astounding properties of biological sensors can often be mapped onto a dynamical system below the occurrence of a bifurcation. For mammalian hearing, a Hopf bifurcation description has been shown to work across a whole range of scales, from individual hair bundles to whole regions of the cochlea. We reveal here the origin of this scale invariance, from a general level, applicable to all dynamics in the vicinity of a Hopf bifurcation (embracing, e.g., neuronal Hodgkin-Huxley equations). When subject to natural "signal coupling," ensembles of Hopf systems below the bifurcation threshold exhibit a collective Hopf bifurcation. This collective Hopf bifurcation occurs at parameter values substantially below where the average of the individual systems would bifurcate, with a frequency profile that is sharpened if compared to the individual systems.

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“…These packets not only capture fundamental properties of speech, they also allow a very efficient computational implementation by means of the matching pursuit algorithm. The general approach leads to a source estimate of the auditory object that could then be used to tune the mammalian hearing sensor, the cochlea (Martignoli, Vyver, Kern, Uwate, & Stoop, 2007;Stoop, Jasa, Uwate, & Martignoli, 2007), and potentially also some of the auditory nuclei, toward the desired object (see Figure 1).…”

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Principles and Typical Computational Limitations of Sparse Speaker Separation Based on Deterministic Speech Features

Kern

Stoop

2011

Neural Computation

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Year: 2011Principles and typical computational limitations of sparse speaker separation based on deterministic speech features Kern The separation of mixed auditory signals into their sources is an eminent neuroscience and engineering challenge. We reveal the principles underlying a deterministic, neural network-like solution to this problem. This approach is orthogonal to ICA/PCA, which views the signal constituents as independent realizations of random processes. We demonstrate exemplarily that in the absence of salient frequency modulations, the decomposition of speech signals into local cosine packets allows for a sparse, noise-robust speaker separation. As the main result, we present analytical limitations inherent in the approach, where we propose strategies of how to deal with this situation. Our results offer new perspectives toward efficient noise cleaning and auditory signal separation and provide a new perspective of how the brain might achieve these tasks.

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Analog electronic cochlea with mammalian hearing characteristics

Cited by 46 publications

References 15 publications

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

Signal-Coupled Subthreshold Hopf-Type Systems Show a Sharpened Collective Response

Principles and Typical Computational Limitations of Sparse Speaker Separation Based on Deterministic Speech Features

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