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Nearest neighbours reveal fast and slow components of motor learning 期刊论文
NATURE, 2020, 577 (7791) : 526-+
作者:  Kollmorgen, Sepp;  Hahnloser, Richard H. R.;  Mante, Valerio
收藏  |  浏览/下载:16/0  |  提交时间:2020/07/03

A new method for analysing change in high-dimensional data is based on nearest-neighbour statistics and is applied here to song dynamics during vocal learning in zebra finches, but could potentially be applied to other biological and artificial behaviours.


Changes in behaviour resulting from environmental influences, development and learning(1-5) are commonly quantified on the basis of a few hand-picked features(2-4,6,7) (for example, the average pitch of acoustic vocalizations(3)), assuming discrete classes of behaviours (such as distinct vocal syllables)(2,3,8-10). However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics(11-13), and apply it to song development in a songbird, the zebra finch(3). First, we introduce the concept of '  repertoire dating'  , whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into '  regressions'  , '  anticipations'  and '  typical renditions'  . Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified '  behavioural trajectory'  , revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,14,15) across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself-rather than to a potentially arbitrary, experimenter-defined goal(2,3,14,16)-appears well suited for comparing learning and change across behaviours and species(17,18), as well as biological and artificial systems(5).


  
Loopy Levy flights enhance tracer diffusion in active suspensions 期刊论文
NATURE, 2020, 579 (7799) : 364-+
作者:  Hu, Bo;  Jin, Chengcheng;  Zeng, Xing;  Resch, Jon M.;  Jedrychowski, Mark P.;  Yang, Zongfang;  Desai, Bhavna N.;  Banks, Alexander S.;  Lowell, Bradford B.;  Mathis, Diane;  Spiegelman, Bruce M.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

A theoretical framework describing the hydrodynamic interactions between a passive particle and an active medium in out-of-equilibrium systems predicts long-range Levy flights for the diffusing particle driven by the density of the active component.


Brownian motion is widely used as a model of diffusion in equilibrium media throughout the physical, chemical and biological sciences. However, many real-world systems are intrinsically out of equilibrium owing to energy-dissipating active processes underlying their mechanical and dynamical features(1). The diffusion process followed by a passive tracer in prototypical active media, such as suspensions of active colloids or swimming microorganisms(2), differs considerably from Brownian motion, as revealed by a greatly enhanced diffusion coefficient(3-10) and non-Gaussian statistics of the tracer displacements(6,9,10). Although these characteristic features have been extensively observed experimentally, there is so far no comprehensive theory explaining how they emerge from the microscopic dynamics of the system. Here we develop a theoretical framework to model the hydrodynamic interactions between the tracer and the active swimmers, which shows that the tracer follows a non-Markovian coloured Poisson process that accounts for all empirical observations. The theory predicts a long-lived Levy flight regime(11) of the loopy tracer motion with a non-monotonic crossover between two different power-law exponents. The duration of this regime can be tuned by the swimmer density, suggesting that the optimal foraging strategy of swimming microorganisms might depend crucially on their density in order to exploit the Levy flights of nutrients(12). Our framework can be applied to address important theoretical questions, such as the thermodynamics of active systems(13), and practical ones, such as the interaction of swimming microorganisms with nutrients and other small particles(14) (for example, degraded plastic) and the design of artificial nanoscale machines(15).


  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:30/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).


  
Enhanced sprout-regeneration offsets warming-induced forest mortality through shortening the generation time in semiarid birch forest 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2018, 409: 298-306
作者:  Xu, Chongyang;  Liu, Hongyan;  Zhou, Mei;  Xue, Jiaxin;  Zhao, Pengwu;  Shi, Liang;  Shangguan, Huailiang
收藏  |  浏览/下载:17/0  |  提交时间:2019/04/09
Arid timberline  Arid tree line  Biological features  Climate change  Patch size  White birch