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Nature封面故事: 想与做背后的神经活动

摘要 : 美国匹兹堡大学和匹兹堡神经基础认知中心等处的研究人员以恒河猴为模型,发现大脑已有的神经网络限制了神经元生成新模式的能力,这种限制决定了学习的容易程度。这项研究发现有望为治疗中风和其他大脑损伤提供一种新的途径。相关文章发表于2014年8月27日的《Nature》杂志上。
Nature封面故事: 想与做背后的神经活动

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。



Neural constraints on learning

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

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 paradigm, 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), wherin 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.

对应none杂志: 2014年8月28日Nature杂志精选

来源: Nature 浏览次数:117


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