Mammalian cochlea as a physics guided evolution-optimized hearing sensor

Note on the Reliability of Biological vs. Artificial Neural Networks

Various types of neural networks are currently widely used in diverse technical applications, not least because neural networks are known to be able to “generalize.” The latter property raises expectations that they should be able to handle unexpected situations with similar success than humans. Using fundamental examples, we show that in situations for which they have not been trained, artificial approaches tend to run into substantial problems, which highlights a deficit in comparisons to human abilities. For this problem–which seems to have obtained little attention so far–we provide a first analysis, based on simple examples, which exhibits some key features responsible for the difference between human and artificial intelligence.

Bioinspired Electronics for Artificial Sensory Systems

Humans have a myriad of sensory receptors in different sense organs that form the five traditionally recognized senses of sight, hearing, smell, taste, and touch. These receptors detect diverse stimuli originating from the world and turn them into brain-interpretable electrical impulses for sensory cognitive processing, enabling us to communicate and socialize. Developments in biologically inspired electronics have led to the demonstration of a wide range of electronic sensors in all five traditional categories, with the potential to impact a broad spectrum of applications. Here, recent advances in bioinspired electronics that can function as potential artificial sensory systems, including prosthesis and humanoid robots are reviewed. The mechanisms and demonstrations in mimicking biological sensory systems are individually discussed and the remaining future challenges that must be solved for their versatile use are analyzed. Recent progress in bioinspired electronic sensors shows that the five traditional senses are successfully mimicked using novel electronic components and the performance regarding sensitivity, selectivity, and accuracy have improved to levels that outperform human sensory organs. Finally, neural interfacing techniques for connecting artificial sensors to the brain are discussed.

Frequency sensitivity in mammalian hearing from a fundamental nonlinear physics model of the inner ear

A dominant view holds that the outer and middle ear are the determining factors for the frequency dependence of mammalian hearing sensitivity, but this view has been challenged. In the ensuing debate, there has been a missing element regarding in what sense and to what degree the biophysics of the inner ear might contribute to this frequency dependence. Here, we show that a simple model of the inner ear based on fundamental physical principles, reproduces, alone, the experimentally observed frequency dependence of the hearing threshold. This provides direct cochlea modeling support of the possibility that the inner ear could have a substantial role in determining the frequency dependence of mammalian hearing.

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

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.

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

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.