Multiple timescales in the adaptation of the rotational VOR

被引:13
|
作者
Colagiorgio, Paolo [1 ]
Bertolini, Giovanni [1 ,2 ]
Bockisch, Christopher J. [2 ,3 ,4 ]
Straumann, Dominik [2 ]
Ramat, Stefano [1 ]
机构
[1] Univ Pavia, Dept Elect Comp & Biomed Engn, I-27100 Pavia, Italy
[2] Univ Zurich Hosp, Dept Neurol, CH-8091 Zurich, Switzerland
[3] Univ Zurich Hosp, Dept Ophthalmol, CH-8091 Zurich, Switzerland
[4] Univ Zurich Hosp, Dept Otorhinolaryngol, CH-8091 Zurich, Switzerland
关键词
VOR adaptation; multiple timescales; motor learning; internal models; VOR adaptation model; VESTIBULOOCULAR REFLEX ADAPTATION; SACCADIC EYE-MOVEMENTS; SHORT-TERM; INTERNAL-MODELS; SMOOTH-PURSUIT; ERROR SIGNALS; MOTOR CONTROL; CEREBELLAR FLOCCULUS; SPONTANEOUS-RECOVERY; ADAPTIVE-CONTROL;
D O I
10.1152/jn.00688.2014
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Goal-directed movements, such as pointing and saccades, have been shown to share similar neural architectures, in spite of the different neuromuscular systems producing them. Such structure involve an inverse model of the actuator being controlled, which produces the commands innervating the muscles, and a forward model of the actuator, which predicts the sensory consequences of such commands and allows online movement corrections. Recent studies have shown that goal-directed movements also share similar motor-learning and motor-memory mechanisms, which are based on multiple timescales. The hypothesis that also the rotational vestibulo-ocular reflex (rVOR) may be based on a similar architecture has been presented recently. We hypothesize that multiple timescales are the brain's solution to the plasticity-stability dilemma, allowing adaptation to temporary and sudden changes while keeping stable motor-control abilities. If that were the case, then we would also expect the adaptation of reflex movements to follow the same principles. Thus we studied rVOR gain adaptation in eight healthy human subjects using a custom paradigm aimed at investigating the existence of spontaneous recovery, which we considered as the hallmark of multiple timescales in motor learning. Our experimental results show that spontaneous recovery occurred in six of eight subjects. Thus we developed a mathematical model of rVOR adaptation based on two hidden-states processes, which adapts the cerebellar-forward model of the ocular motor plant, and show that it accurately simulates our experimental data on rVOR gain adaptation, whereas a single timescale learning process fails to do so.
引用
收藏
页码:3130 / 3142
页数:13
相关论文
共 50 条
  • [41] Distinct mechanisms control contrast adaptation over different timescales
    Bao, Min
    Fast, Elizabeth
    Mesik, Juraj
    Engel, Stephen
    JOURNAL OF VISION, 2013, 13 (10):
  • [42] Hierarchical gradients of multiple timescales in the mammalian forebrain
    Songa, Min
    Shin, Eun Ju
    Seod, Hyojung
    Soltanie, Alireza
    Steinmetz, Nicholas A.
    Lee, Daeyeol
    Jung, Min Whan
    Paikk, Se-Bum
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2024, 121 (51)
  • [43] Insect olfactory coding and memory at multiple timescales
    Gupta, Nitin
    Stopfer, Mark
    CURRENT OPINION IN NEUROBIOLOGY, 2011, 21 (05) : 768 - 773
  • [44] Analysis of periodic updating for systems with multiple timescales
    Browning, GL
    Kreiss, HO
    JOURNAL OF THE ATMOSPHERIC SCIENCES, 1996, 53 (02) : 335 - 348
  • [45] Rapid Learning of Temporal Dependencies at Multiple Timescales
    Smith, Cybelle M.
    Thompson-Schill, Sharon L.
    Schapiro, Anna C.
    JOURNAL OF COGNITIVE NEUROSCIENCE, 2024, 36 (11) : 2343 - 2356
  • [46] Sequential Modeling of Topic Dynamics with Multiple Timescales
    Iwata, Tomoharu
    Yamada, Takeshi
    Sakurai, Yasushi
    Ueda, Naonori
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2012, 5 (04)
  • [47] Critical period regulation across multiple timescales
    Reh, Rebecca K.
    Dias, Brian G.
    Nelson, Charles A., III
    Kaufer, Daniela
    Werker, Janet F.
    Kolb, Bryan
    Levine, Joel D.
    Hensch, Takao K.
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (38) : 23242 - 23251
  • [48] Face Recognition: Canonical Mechanisms at Multiple Timescales
    Giese, Martin A.
    CURRENT BIOLOGY, 2016, 26 (13) : R534 - R537
  • [49] Learning Multiple Timescales in Recurrent Neural Networks
    Alpay, Tayfun
    Heinrich, Stefan
    Wermter, Stefan
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I, 2016, 9886 : 132 - 139
  • [50] Multiple timescales in a model for DNA denaturation dynamics
    Baiesi, Marco
    Livi, Roberto
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2009, 42 (08)