Neural manifold under plasticity in a goal driven learning behaviour.
Neural manifold under plasticity in a goal driven learning behaviour.
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目标驱动学习行为中可塑性下的神经流形。
DOI：
10.1371/journal.pcbi.1008621
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Clopath C
中科院分区：
文献类型：
--
作者：
Feulner B;Clopath C
神经活动通常是低维的，并且只受少数几个显著的神经协变模式支配。有人假设，这些协变模式可以形成用于快速和灵活的运动控制的积木。支持这一观点的是，最近的实验表明，猴子可以学习在几分钟的时间尺度上调整它们在运动皮质中的神经活动，因为这种变化位于原始的低维子空间内，也被称为神经流形。然而，这种流形内适应的神经机制仍不清楚。在这里，我们在一个计算模型中表明，在学习反馈信号的驱动下，对递归权重的修改可以解释内部流形学习和外部流形学习之间观察到的行为差异。我们的发现提供了一个新的视角，表明反复的体重变化并不一定会导致神经流形的变化。相反，成功的学习自然被限制在一个公共子空间中。有研究表明，神经元的协调激活可能在运动执行中发挥重要作用。这种活动模式是固定的，还是灵活地重新学习，仍然是一个有争议的问题。研究表明，猴子只要使用一套初始的活动模式，就可以在几分钟内学会调整自己的神经活动。相比之下，猴子需要几天的时间和一系列的训练程序来学习全新的模式。在这里，我们开发了一个计算模型来调查哪些生物学特征可能导致这些实验观察。在我们的模型中，学习是通过递归连接的网络中神经元之间的权重变化来实现的。为了让这些重量变化改善产生的行为，需要一个误差信号，它告诉每个神经元它应该增加还是减少它的活动，以便产生更接近目标运动的运动。我们发现，只有在第一个实验条件下，猴子才有可能学习这样的错误信号，即猴子需要使用已经存在的活动模式来调整他们的神经活动。因此，这种错误信号的学习对神经活动中的哪种类型的变化可以学习和不能学习构成了主要的限制。
Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace. It has been suggested that the coordinated activation of neurons might play an important role for movement execution. Whether such activity patterns are fixed or flexibly relearned remains matter of debate. It has been shown that monkeys can learn within minutes to adjust their neural activity, as long as they use the initial set of activity patterns. In contrast, monkeys needed several days and a sequential training procedure to learn completely new patterns. Here, we developed a computational model to investigate which biological features might lead to these experimental observations. Learning in our model is implemented through weight changes between neurons in a recurrently connected network. In order for these weight changes to improve the produced behaviour, an error signal is required which tells each neuron whether it should increase or decrease its activity in order to produce a movement closer to the target movement. We found that learning such an error signal is possible only in the first experimental condition, where monkeys needed to adapt their neural activity using already existing activity patterns. The learning of this error signal therefore poses a major constraint on what type of changes in neural activity can and can not be learned.
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通讯作者：
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影响因子：
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