Auditory power-law activation-avalanches exhibit a fundamental computational ground-state

The cochlea provides a biological information-processing paradigm that we only begin to under- stand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynami- cal nodes, on which even simple sound input triggers subnetworks of activated elements that follow power-law size statistics ('avalanches'). From dynamical systems theory, power-law size distribu- tions 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.Comment: Videos are not included, please ask author

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

The study of complex networks has pursued an understanding of macroscopic behavior by focusing on power-laws in microscopic observables. Here, we uncover two universal fundamental physical principles that are at the basis of complex networks 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.Comment: 14 pages, 7 figure

Mammalian cochlea as a physics guided evolution-optimized hearing sensor

Nonlinear physics plays an essential role in hearing, from sound signal generation to sound sensing to the processing of complex sound environments. We demonstrate that the evolution of the biological hearing sensors demonstrates a dramatic reduction in the solution space available for hearing sensors due to nonlinear physics principles. More specifically, our analysis hints at that the differences between amniotic lineages hearing, could be recast into a scaleable and a non-scaleable arrangement of nonlinear sound detectors. The scalable solution employed in mammals, as the most advanced design, provides a natural context that demands the ultimate characterization of complex sounds through pitch

Human pitch is pre-cortical: The essential role of 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.Comment: 12 pages, 4 figure

Phenotypic selection on floral scent: trade-off between attraction and deterrence?

Flowers emit a large variety of floral signals that play a fundamental role in the communication of plants with their mutualists and antagonists. We investigated phenotypic selection on floral scent and floral display using the rewarding orchid species Gymnadenia odoratissima. We found positive directional selection on inflorescence size, as well as positive and negative selection on floral scent compounds. Structural equation modeling showed that "active” compounds, i.e. those that were shown in earlier investigations to be detected by pollinator insects, were positively linked to fitness, whereas "non-active” were negatively linked to fitness. Our results suggest that different patterns of selection impact on different scent compounds, which may relate to the functions of compounds for attracting/deterring insect

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. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition

State-of-the-art ASR systems have achieved promising results by modeling local and global interactions separately. While the former can be computed efficiently, global interactions are usually modeled via attention mechanisms, which are expensive for long input sequences. Here, we address this by extending HyperMixer, an efficient alternative to attention exhibiting linear complexity, to the Conformer architecture for speech recognition, leading to HyperConformer. In particular, multi-head HyperConformer achieves comparable or higher recognition performance while being more efficient than Conformer in terms of inference speed, memory, parameter count, and available training data. HyperConformer achieves a word error rate of 2.9% on Librispeech test-clean with less than 8M neural parameters and a peak memory during training of 5.7GB, hence trainable with accessible hardware. Encoder speed is between 38% on mid-length speech and 56% on long speech faster than an equivalent Conformer. (The HyperConformer recipe is publicly available in: https://github.com/speechbrain/speechbrain/tree/develop/recipes/LibriSpeech/ASR/transformer/)Comment: Florian Mai and Juan Zuluaga-Gomez contributed equally. To appear in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 202

Complete solution of the exponential Diophantine equation (P_n^x+P_{n+1}^x=P_m^y)

In this paper, we find all the solutions of the title Diophantine equation in positive integer variables (m, n, x,y), where (P_k) is the kth term of the Pell sequence

Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model streptomycetes

We are indebted with Marnix Medema, Paul Straight and Sean Rovito, for useful discussions and critical reading of the manuscript, as well as with Alicia Chagolla and Yolanda Rodriguez of the MS Service of Unidad Irapuato, Cinvestav, and Araceli Fernandez for technical support in high-performance computing. This work was funded by Conacyt Mexico (grants No. 179290 and 177568) and FINNOVA Mexico (grant No. 214716) to FBG. PCM was funded by Conacyt scholarship (No. 28830) and a Cinvestav posdoctoral fellowship. JF and JFK acknowledge funding from the College of Physical Sciences, University of Aberdeen, UK.Peer reviewedPublisher PD