Neural constraints on learning.
Neural constraints on learning.
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DOI：
10.1038/nature13665
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发表时间：
2014-08-28
期刊：
影响因子：
64.8
通讯作者：
Batista, Aaron P.
中科院分区：
文献类型：
--
作者：
Sadtler, Patrick T.;Quick, Kristin M.;Golub, Matthew D.;Chase, Steven M.;Ryu, Stephen I.;Tyler-Kabara, Elizabeth C.;Yu, Byron M.;Batista, Aaron P.
运动、感觉和认知学习需要神经元网络来产生新的活动模式。因为有些行为比其他行为更容易学习，我们想知道是否有些神经活动模式比其他模式更容易产生。我们问现有的网络是否限制了它的神经元子集能够表现出的模式，如果是这样，什么原则定义了约束。我们采用了一个闭环的皮层内脑-机接口（BCI）的学习范式，其中恒河猴控制的计算机光标通过调节在初级运动皮层的神经活动模式。使用BCI范例，我们可以指定和改变神经活动如何映射到光标速度。在每个阶段开始时，我们观察记录的神经群体的特征活动模式。这些模式包括高维神经放电率空间内的低维空间（称为内在流形，或IM）。它们可能反映了底层神经回路施加的限制。我们发现，动物可以很容易地学会熟练地控制光标使用的神经活动模式，在IM。然而，动物不太能够学会熟练地控制光标使用的活动模式，是外部的IM。这一结果表明，网络的现有结构可以塑造学习。在小时的时间尺度上，似乎很难学习生成与现有网络结构不一致的神经活动模式。这些发现为我们的观察提供了一个网络层面的解释，即当新技能与我们已经拥有的技能相关时，我们更容易学习新技能。
Motor, sensory, and cognitive learning require networks of neurons to generate new activity patterns. Because some behaviors are easier to learn than others, we wondered if some neural activity patterns are easier to generate than others. We asked whether the existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define the constraint. We employed a closed-loop intracortical brain-computer interface (BCI) learning paradigm in which Rhesus monkeys controlled a computer cursor by modulating neural activity patterns in primary motor cortex. Using the BCI 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. These patterns comprise a low-dimensional space (termed the intrinsic manifold, or IM) within the high-dimensional neural firing rate space. They presumably reflect constraints imposed by the underlying neural circuitry. We found that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the IM. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the IM. This result suggests that the existing structure of a network can shape learning. On the timescale of hours, it appears 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.
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影响因子：
8.2
作者：
Park DC;Lodi-Smith J;Drew L;Haber S;Hebrank A;Bischof GN;Aamodt W
通讯作者：
Aamodt W
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
9.2
作者：
Hwang, Eun Jung;Bailey, Paul M.;Andersen, Richard A.
通讯作者：
Andersen, Richard A.
DOI：
10.1073/pnas.0808113105
发表时间：
2008-12-09
影响因子：
11.1
作者：
Jarosiewicz, Beata;Chase, Steven M.;Schwartz, Andrew B.
通讯作者：
Schwartz, Andrew B.
影响因子：
5.3
作者：
Ranganathan, Rajiv;Wieser, Jon;Scheidt, Robert A.
通讯作者：
Scheidt, Robert A.