Nearest neighbours reveal fast and slow components of motor learning.
Nearest neighbours reveal fast and slow components of motor learning.
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DOI：
10.1038/s41586-019-1892-x
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发表时间：
2020-01
期刊：
影响因子：
64.8
通讯作者：
Mante V
中科院分区：
文献类型：
--
作者：
Kollmorgen S;Hahnloser RHR;Mante V
由于环境影响、发育和学习而导致的行为变化通常基于一些精心挑选的特定领域特征（例如声学发声的平均音高）并假设行为的离散类别（例如不同的语音音节）进行量化。这种方法在不同的行为和模型系统之间的通用性很差，可能会错过重要的变化组件。在这里，我们基于最近邻统计数据对行为变化进行了更一般的描述，并将其应用于鸣禽斑胸草雀的歌曲发展。首先，我们介绍了“剧目约会”，即每个演出的行为（例如，每个发声）被分配一个剧目的时间，反映了类似的演出时，典型的行为剧目。保留时间（Repertoire time，rT）是指在发声学习和发育过程中，与发声的长期变化相一致的发声变异性成分，它将发声行为保留分为回归（rT <真实发声时间，t）、预期（rT > t）和典型再现（rT> t）。其次，我们获得了一个整体的，但低维的，描述的声音变化的分层“行为轨迹”，揭示了多个，以前未被识别的，组件的行为变化的快速和缓慢的时间尺度，以及不同的模式过夜巩固。回归的日变化只经历弱巩固，而预期和典型的再现巩固充分。由于它的普遍性，我们对行为如何相对于自身而不是相对于一个潜在的任意的、实验者定义的目标进化的非参数描述似乎非常适合于比较行为和物种之间的学习和变化，以及生物和人工系统。
Changes in behavior, due to environmental influences, development, and learning, are commonly quantified based on a few hand-picked, domain-specific, features (e.g. the average pitch of acoustic vocalizations) and assuming discrete classes of behaviors (e.g. distinct vocal syllables). Such methods generalize poorly across different behaviors and model systems and may miss important components of change. Here we present a more general account of behavioral change based on nearest-neighbor statistics and apply it to song development in a songbird, the zebra finch. First, we introduce “repertoire dating”, whereby each rendition of a behavior (e.g. each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioral repertoire. Repertoire time (rT) isolates the components of vocal variability congruent with the long-term changes due to vocal learning and development and stratifies the behavioral repertoire into regressions (rT < true production time, t), anticipations (rT > t), and typical renditions (rT ≈ t). Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified “behavioral trajectory”, revealing multiple, previously unrecognized, components of behavioral change on fast and slow timescales, as well as distinct patterns of overnight consolidation. Diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our non-parametric description of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal appears well-suited to compare learning and change across behaviors and species, as well as biological and artificial systems.
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影响因子：
64.8
作者：
Shank, Sylvan S.;Margoliash, Daniel
通讯作者：
Margoliash, Daniel
影响因子：
64.8
作者：
Derégnaucourt, S;Mitra, PP;Tchernichovski, O
通讯作者：
Tchernichovski, O
影响因子：
16.2
作者：
Klaus A;Martins GJ;Paixao VB;Zhou P;Paninski L;Costa RM
通讯作者：
Costa RM
DOI：
10.1073/pnas.0903214106
发表时间：
2009-07-28
影响因子：
11.1
作者：
Andalman, Aaron S.;Fee, Michale S.
通讯作者：
Fee, Michale S.
影响因子：
25
作者：
Korman, Maria;Doyon, Julien;Karni, Avi
通讯作者：
Karni, Avi