Florian Gomez

Pitch sensation involves stochastic resonance

Pitch is a complex hearing phenomenon that results from elicited and self-generated cochlear vibrations. Read-off vibrational information is relayed higher up the auditory pathway, where it is then condensed into pitch sensation. How this can adequately be described in terms of physics has largely remained an open question. We have developed a peripheral hearing system (in hardware and software) that reproduces with great accuracy all salient pitch features known from biophysical and psychoacoustic experiments. At the level of the auditory nerve, the system exploits stochastic resonance to achieve this performance, which may explain the large amount of noise observed in the working auditory nerve.

Two universal physical principles shape the power-law statistics of real-world networks

The study of complex networks has pursued an understanding of macroscopic behaviour by focusing on power-laws in microscopic observables. Here, we uncover two universal fundamental physical principles that are at the basis of complex network generation. These principles together predict the generic emergence of deviations from ideal power laws, which were previously discussed away by reference to the thermodynamic limit. Our approach proposes a paradigm shift in the physics of complex networks, toward the use of power-law deviations to infer meso-scale structure from macroscopic observations.

Universal dynamical properties preclude standard clustering in a large class of biochemical data

MOTIVATION Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties. RESULTS The variety of dynamical behaviors that can be exhibited by complex biochemical reactions on variation of the system parameters are fundamental system fingerprints. In parameter space, shrimp-like or swallow-tail structures separate parameter sets that lead to stable periodic dynamical behavior from those leading to irregular behavior. We work out the genericity of this phenomenon and demonstrate novel examples for their occurrence in realistic models of biophysics. Although we elucidate the phenomenon by considering the emergence of periodicity in dependence on system parameters in a low-dimensional parameter space, the conclusions from our simple setting are shown to continue to be valid for features in a higher-dimensional feature space, as long as the feature-generating mechanism is not too extreme and the dimension of this space is not too high compared with the amount of available data. AVAILABILITY AND IMPLEMENTATION For online versions of super-paramagnetic clustering see http://stoop.ini.uzh.ch/research/clustering. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Macroscopic bursting in physiological networks: node or network property?

Activity pattern modalities of neuronal ensembles are determined by node properties as well as network structure. For many purposes, it is of interest to be able to relate activity patterns to either node properties or to network properties (or to a combination of both). When in physiological neural networks we observe bursting on a coarse-grained time and space scale, a proper decision on whether bursts are the consequence of individual neurons with an inherent bursting property or whether we are dealing with a genuine network effect has generally not been possible because of the noise in these systems. Here, by linking different orders of time and space scales, we provide a simple coarse-grained criterion for deciding this question.

Mammalian cochlea as a physics guided evolution-optimized hearing sensor

Nonlinear physics plays an essential role in hearing. We demonstrate on a mesoscopic description level that during the evolutionary perfection of the hearing sensor, nonlinear physics led to the unique design of the cochlea observed in mammals, and that this design requests as a consequence the perception of pitch. Our insight challenges the view that mostly genetics is responsible for the uniformity of the construction of the mammalian hearing sensor. Our analysis also suggests that scaleable and non-scaleable arrangements of nonlinear sound detectors may be at the origin of the differences between hearing sensors in amniotic lineages.

Mammalian pitch sensation shaped by the cochlear fluid

The perceived pitch of a complex harmonic sound changes if the partials of the sound are frequency-shifted by a fixed amount. Simple mathematical rules that the perceived pitch could be expected to follow (’first pitch-shift’) are violated in psychoacoustic experiments (’second pitchshift’). For this, commonly cognitive cortical processes were held responsible. Here, we show that human pitch perception can be reproduced from a minimal, purely biophysical, model of the cochlea, by fully recovering the psychoacoustical pitch-shift data of G.F. Smoorenburg (1970) and related physiological measurements from the cat cochlear nucleus. For this to happen, the cochlear fluid plays a distinguished role.

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.

Complex Networks of Harmonic Structure in Classical Music

Music is a ubiquitous, complex and defining phenomenon of human culture. We create and analyze complex networks representing harmonic transitions in eight selected compositions of Johann Sebastian Bach’s Well-Tempered Clavier. While all resulting networks exhibit the typical ‘small-world’-characteristics, they clearly differ in their degree distributions. Some of the degree distributions are well fit by a power-law, others by an exponential, and some by neither. This seems to preclude the necessity of a scale-free degree distribution for music to be appealing. To obtain a quality measure for the network representation, we design a simple algorithm that generates artificial polyphonic music, which also exhibits the different styles of composition underlying the various pieces.

Tuning the Hopf cochlea towards listening

In a Hopf cochlea, coupled Hopf oscillators of individual frequency each, account for the active amplification of the auditory input. All salient nonlinear aspects of hearing can be traced back to the physical properties of the Hopf oscillators. At each location along the cochlea, the amplification strength is effectively governed by a single real parameter characterizing the distance of the Hopf oscillator from the Hopf-bifurcation point. Using these parameters, given a mixture of input signals (e.g., a set of musical instruments) it should be possible to tune the cochlea towards a single sound component. Introducing an autocorrelation-based tuning measure, we demonstrate the tunability of the Hopf Cochlea on recorded real-life instruments of different timbres and pitches. Despite the strongly nonlinear and therefore interaction-prone nature of the device, strong and simple tuning patterns permit an easy tuning to sounds of varying pitch. Our insights may prove essential for gaining further understanding...

Deviation from Criticality in Functional Biological Networks

Claims based on power laws that cognition occurs in a critical state often rely on the assumption that the network observables studied are observables of cognition, however this relationship to function is not clear. Our novel approach to investigate this problem is instead to consider functional output during (goal-directed) pre-copulatory courtship of Drosophila melanogaster, which we study as a complex network. This courtship body language, expressed through a symbolic dynamics, has previously been shown to be situation specific and grammatically complex; here, we show that the networks underlying it deviate from a scale-free structure when recursive grammars are included. This structural deviation is modelled by a simple network growth algorithm which adds internal edge saturation to the preferential attachment paradigm. From this, we suggest that a critical state may not be compatible with higher level cognition.