scholarly journals Neighborhood-statistics reveal complex dynamics of song acquisition in the zebra finch

2019 ◽  
Author(s):  
Sepp Kollmorgen ◽  
Richard Hahnloser ◽  
Valerio Mante
Keyword(s):  
Zebra Finch ◽  
Behavioral Change ◽  
Nearest Neighbor ◽  
Complex Dynamics ◽  
Vocal Learning ◽  
Behavioral Repertoire ◽  
Yield Model ◽  
Production Time ◽  
Model Free ◽  
Song Acquisition

ABSTRACTMotor behaviors are continually shaped by a variety of processes such as environmental influences, development, and learning1,2. The resulting behavioral changes are commonly quantified based on hand-picked features3–10(e.g. syllable pitch11) and assuming discrete classes of behaviors (e.g. distinct syllables)3–5,9,10,12–17. Such methods may generalize poorly across behaviors and species and are necessarily biased. Here we present an account of behavioral change based on nearest-neighbor statistics18–23that avoids such biases and apply it to song development in the juvenile zebra finch3. First, we introduce the concept ofrepertoire dating, whereby each syllable rendition isdatedwith a “pseudo” production-day corresponding to the day when similar renditions were typical in the behavioral repertoire. Differences in pseudo production-day across renditions isolate the components of vocal variability congruent with the long-term changes due to vocal learning and development. This variability is large, as about 10% of renditions have pseudo production-days falling more than 10 days into the future (anticipations) or into the past (regressions) relative to their actual production time. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of abehavioral trajectory, which reproduces the pairwise similarities between renditions grouped by production time and pseudo production-day24. The behavioral trajectory reveals multiple, previously unrecognized components of behavioral change operating at distinct time-scales. These components interact differently across the behavioral repertoire—diurnal change in regressions undergoes only weak overnight consolidation4,5, whereas anticipations and typical renditions consolidate fully2,6,25. Our nearest-neighbor methods yield model-free descriptions of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal3–5,11. Because of their generality, our methods appear well-suited to comparing learning across behaviors and species1,26–32, and between biological and artificial systems.

scholarly journals Behavior-Linked FoxP2 Regulation Enables Zebra Finch Vocal Learning

2015 ◽  
Vol 35 (7) ◽  
pp. 2885-2894 ◽  
Author(s):  
J. B. Heston ◽  
S. A. White
Keyword(s):  
Zebra Finch ◽  
Vocal Learning

scholarly journals Coloring Single Nanoparticle Trajectory in Live Cell with its Own History: a Presuppositionless Preprocessing Approach

2019 ◽  
Author(s):  
Hansen Zhao ◽  
Zhenrong Huang ◽  
Feng Ge ◽  
Xiangjun Shi ◽  
Bin Xiong ◽  
...  
Keyword(s):  
Single Particle ◽  
Complex Dynamics ◽  
Live Cell ◽  
State Transitions ◽  
Human Beings ◽  
Global Pattern ◽  
Model Free ◽  
Mechanical Models ◽  
Data Points ◽  
Underlying Mechanisms

AbstractAnalyzing single particle trajectories is a prominent issue in understanding complex dynamics such as nanoparticle-cell interactions. Existing methods treat data points as isolated “atoms” and use predefined mechanical models to “frame” their complicated relationship. Herein, we propose a “historical evolution” based model-free strategy. It allows spatiotemporal heterogeneity embedded in a trajectory to self-emerge as consecutive colored segments before any model assumption, provide both an overall picture and local state transitions on the particle movement with minimum information loss, and inspire further model-based investigation. We demonstrate with simulations and experiments that the underlying mechanisms of various time-series and motion states of single nanoparticles on live cell membranes could all be revealed successfully. Since complexity studies at different levels of molecules, particles, cells, human beings, vehicles, and even stars could all be reduced to analyzing spatiotemporal trajectories of “single particles”, this presuppositionless approach will help fundamental researches on many important systems.Impact StatementA preprocessing strategy for single particle trajectory analysis is established by providing an intuitive global pattern from “historical experiences” of the particle without predefining any mechanical models.

scholarly journals Model-free reconstruction of magnetic correlations in frustrated magnets

IUCrJ ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 410-416 ◽  
Author(s):  
Nikolaj Roth ◽  
Andrew F. May ◽  
Feng Ye ◽  
Bryan C. Chakoumakos ◽  
Bo Brummerstedt Iversen
Keyword(s):  
Atomic Structure ◽  
Nearest Neighbor ◽  
Function Analysis ◽  
Three Dimensional ◽  
Scattering Data ◽  
Frustrated Magnets ◽  
Magnetic Correlations ◽  
Model Free ◽  
Frustrated Magnet ◽  
Modelling Techniques

Frustrated magnetic systems exhibit extraordinary physical properties, but quantification of their magnetic correlations poses a serious challenge to experiment and theory. Current insight into frustrated magnetic correlations relies on modelling techniques such as reverse Monte-Carlo methods, which require knowledge about the exact ordered atomic structure. Here, we present a method for direct reconstruction of magnetic correlations in frustrated magnets by three-dimensional difference pair distribution function analysis of neutron total scattering data. The methodology is applied to the disordered frustrated magnet bixbyite, (Mn1−x Fe x )2O3, which reveals nearest-neighbor antiferromagnetic correlations for the metal sites up to a range of approximately 15 Å. Importantly, this technique allows for magnetic correlations to be determined directly from the experimental data without any assumption about the atomic structure.

scholarly journals Seasonal regulation of singing-driven gene expression associated with song plasticity in the canary, an open-ended vocal learner

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Shin Hayase ◽  
Chengru Shao ◽  
Masahiko Kobayashi ◽  
Chihiro Mori ◽  
Wan-chun Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Neural Activity ◽  
Molecular Mechanisms ◽  
Vocal Learning ◽  
Motor Nucleus ◽  
Vocal Plasticity ◽  
Learning Capacity ◽  
Induction Rate ◽  
Song Acquisition ◽  
Activity Dependent

AbstractSongbirds are one of the few animal taxa that possess vocal learning abilities. Different species of songbirds exhibit species-specific learning programs during song acquisition. Songbirds with open-ended vocal learning capacity, such as the canary, modify their songs during adulthood. Nevertheless, the neural molecular mechanisms underlying open-ended vocal learning are not fully understood. We investigated the singing-driven expression of neural activity-dependent genes (Arc, Egr1, c-fos, Nr4a1, Sik1, Dusp6, and Gadd45β) in the canary to examine a potential relationship between the gene expression level and the degree of seasonal vocal plasticity at different ages. The expression of these genes was differently regulated throughout the critical period of vocal learning in the zebra finch, a closed-ended song learner. In the canary, the neural activity-dependent genes were induced by singing in the song nuclei throughout the year. However, in the vocal motor nucleus, the robust nucleus of the arcopallium (RA), all genes were regulated with a higher induction rate by singing in the fall than in the spring. The singing-driven expression of these genes showed a similar induction rate in the fall between the first year juvenile and the second year adult canaries, suggesting a seasonal, not age-dependent, regulation of the neural activity-dependent genes. By measuring seasonal vocal plasticity and singing-driven gene expression, we found that in RA, the induction intensity of the neural activity-dependent genes was correlated with the state of vocal plasticity. These results demonstrate a correlation between vocal plasticity and the singing-driven expression of neural activity-dependent genes in RA through song development, regardless of whether a songbird species possesses an open- or closed-ended vocal learning capacity.

Delta-9-THC exposure during zebra finch sensorimotor vocal learning increases cocaine reinforcement in adulthood

2019 ◽  
Vol 185 ◽  
pp. 172764 ◽  
Author(s):  
Ahmed Aldhafiri ◽  
Julien C. Dodu ◽  
Ali Alalawi ◽  
Nariman Emadzadeh ◽  
Ken Soderstrom
Keyword(s):  
Zebra Finch ◽  
Vocal Learning

scholarly journals Hydrodynamic scaling limit of continuum solid-on-solid model

2006 ◽  
Vol 2006 ◽  
pp. 1-37 ◽  
Author(s):  
Anamaria Savu
Keyword(s):  
Surface Diffusion ◽  
Nonlinear Evolution Equation ◽  
Nearest Neighbor ◽  
Complex Dynamics ◽  
Nonlinear Evolution ◽  
Scaling Limit ◽  
Solid Model ◽  
Neighbor Interaction ◽  
Hydrodynamic Scaling ◽  
The Continuum

A fourth-order nonlinear evolution equation is derived from a microscopic model for surface diffusion, namely, the continuum solid-on-solid model. We use the method developed by Varadhan for the computation of the hydrodynamic scaling limit of nongradient models. What distinguishes our model from other models discussed so far is the presence of two conservation laws for the dynamics in a nonperiodic box and the complex dynamics that is not nearest-neighbor interaction. Along the way, a few steps have to be adapted to our new context. As a byproduct of our main result, we also derive the hydrodynamic scaling limit of a perturbation of the continuum solid-on-solid model, a model that incorporates both surface diffusion and surface electromigration.

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