A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.
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DOI：
10.1523/jneurosci.4284-09.2010
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发表时间：
2010-06-23
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影响因子：
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通讯作者：
Maass W
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文献类型：
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作者：
Legenstein R;Chase SM;Schwartz AB;Maass W
最近在脑-机接口实验中显示，运动皮层神经元选择性地改变其调谐特性以补偿由移位解码参数引起的错误。特别是，它示出的解码参数被重新分配的神经元的3D调谐曲线的变化比那些解码参数没有被重新分配的神经元。在这篇文章中，我们提出了一个简单的学习规则，可以重现这种效果。我们的学习规则使用由全局奖励信号和神经元噪声驱动的赫布权重更新。与大多数以前提出的学习规则相比，这种方法不需要外部信息来分离噪声和信号。该学习规则能够在高噪声水平下的生物现实时间段内优化模型系统的性能。此外，当模型参数与在上述脑-计算机接口学习实验期间记录的数据相匹配时，该模型产生与实验中发现的学习效果惊人地相似的学习效果。
It has recently been shown in a brain-computer interface experiment that motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters. In particular, it was shown that the 3D tuning curves of neurons whose decoding parameters were re-assigned changed more than those of neurons whose decoding parameters had not been re-assigned. In this article, we propose a simple learning rule that can reproduce this effect. Our learning rule uses Hebbian weight updates driven by a global reward signal and neuronal noise. In contrast to most previously proposed learning rules, this approach does not require extrinsic information to separate noise from signal. The learning rule is able to optimize the performance of a model system within biologically realistic periods of time under high noise levels. Furthermore, when the model parameters are matched to data recorded during the brain-computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments.