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运动皮层的功能网络重组可以用奖赏调制Hebbian学习来解释
Functional Network Reorganization In Motor Cortex Can Be Explained by Reward-Modulated Hebbian Learning |
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| 课程网址: |
http://videolectures.net/nips09_legenstein_fnr/
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| 主讲教师: |
Robert Legenstein
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| 开课单位: |
格拉茨理工大学
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| 开课时间: |
2010-01-19
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| 课程语种: |
英语
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| 中文简介: |
从运动皮层神经元的活动控制神经假体装置受益于学习效应，其中这些神经元的功能适应于控制任务。最近显示，猴子运动皮层中神经元的调谐特性被选择性地调整，以便补偿其活动的错误解释。特别地，已经表明，优选方向被误解的那些神经元的调谐曲线比其他神经元的改变更多。在本文中，我们表明，可以在简单的学习规则的基础上解释实验观察到的系统的自调整属性。该学习规则利用神经元噪声进行探索，并执行由全局奖励信号调制的Hebbian重量更新。与大多数先前提出的奖励调制的Hebbian学习规则相反，该规则不需要关于什么是噪声和什么是信号的无关知识。学习规则能够在生物实际的时间段和高噪声水平下优化模型系统的性能。当神经元噪声与实验数据拟合时，该模型产生类似于猴子实验中发现的学习效果。
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| 课程简介: |
The control of neuroprosthetic devices from the activity of motor cortex neurons benefits from learning effects where the function of these neurons is adapted to the control task. It was recently shown that tuning properties of neurons in monkey motor cortex are adapted selectively in order to compensate for an erroneous interpretation of their activity. In particular, it was shown that the tuning curves of those neurons whose preferred directions had been misinterpreted changed more than those of other neurons. In this article, we show that the experimentally observed self-tuning properties of the system can be explained on the basis of a simple learning rule. This learning rule utilizes neuronal noise for exploration and performs Hebbian weight updates that are modulated by a global reward signal. In contrast to most previously proposed reward-modulated Hebbian learning rules, this rule does not require extraneous knowledge about what is noise and what is signal. The learning rule is able to optimize the performance of the model system within biologically realistic periods of time and under high noise levels. When the neuronal noise is fitted to experimental data, the model produces learning effects similar to those found in monkey experiments.
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| 关 键 词: |
神经元; 奖励调制; 神经元噪声
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-06：lxf
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| 阅读次数: |
59
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