• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nature >Neural constraints on learning
【24h】

Neural constraints on learning

机译：学习上的神经约束

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

在对神经活动的新模式多大程度上通过学习可以产生所做的一项研究中, Aaron Batista及同事对恒河猴利用运动皮层中不同活动模式学习如何控制电脑光标时的神经网络重新组织进行了分析。一些新的神经活动模式比其他的更容易产生（相应于更容易学会的任务）, 这些可从实验开始时的网络拓扑以数学方式预测出来。作者推测, 这些结果为对行动和思想中适应性和持久性之间的平衡从神经角度做解释提供了一个基础。封面：Jasiek Krzysztofiak/Nature。%Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface par-adigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.
机译：在对神经活动的新模式多大程度上通过学习可以产生所做的一项研究中, Aaron Batista及同事对恒河猴利用运动皮层中不同活动模式学习如何控制电脑光标时的神经网络重新组织进行了分析。一些新的神经活动模式比其他的更容易产生（相应于更容易学会的任务）, 这些可从实验开始时的网络拓扑以数学方式预测出来。作者推测, 这些结果为对行动和思想中适应性和持久性之间的平衡从神经角度做解释提供了一个基础。封面：Jasiek Krzysztofiak/Nature。%Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface par-adigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

著录项

  • 来源
    《Nature》 |2014年第7515期|423-426337|共5页
  • 作者

    Patrick T. Sadtler; Kristin M. Quick; Matthew D. Golub; Steven M. Chase; Stephen I. Ryu; Elizabeth C. Tyler-Kabara; Byron M. Yu; Aaron P. Batista;

  • 作者单位

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA, Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh Pennsylvania 15261, USA;

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA, Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh Pennsylvania 15261, USA;

    Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;

    Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;

    Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA, Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California 94301, USA;

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA, Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA;

    Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;

    Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA, Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA, Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh Pennsylvania 15261, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号