Switching between internal and external modes: A multiscale learning principle.
Switching between internal and external modes: A multiscale learning principle.
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DOI：
10.1162/netn_a_00024
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Schapiro AC
中科院分区：
文献类型：
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作者：
Honey CJ;Newman EL;Schapiro AC
大脑构建内部模型，支持感知，预测和外部世界的行动。大脑中的各个回路也学习它们接收的输入的局部世界的内部模型，以促进有效和鲁棒的表示。这些内部模型是如何学习的？我们建议，学习是促进内部偏置和外部偏置的处理模式之间的不断切换。我们回顾了计算证据，这种模式切换可以产生一个错误信号来驱动学习。然后，我们考虑在不同的神经系统模式切换的实例化的经验证据，范围从海马体的亚秒级波动，整个大脑的觉醒-睡眠交替。我们假设，这些内部/外部切换过程，发生在多个尺度，可以驱动学习在每个尺度。这个框架预测，（a）较慢的模式切换应该与学习更多的时间扩展的输入功能和（B）切换中断应该损害新信息与先验信息的整合。
Brains construct internal models that support perception, prediction, and action in the external world. Individual circuits within a brain also learn internal models of the local world of input they receive, in order to facilitate efficient and robust representation. How are these internal models learned? We propose that learning is facilitated by continual switching between internally biased and externally biased modes of processing. We review computational evidence that this mode-switching can produce an error signal to drive learning. We then consider empirical evidence for the instantiation of mode-switching in diverse neural systems, ranging from subsecond fluctuations in the hippocampus to wake-sleep alternations across the whole brain. We hypothesize that these internal/external switching processes, which occur at multiple scales, can drive learning at each scale. This framework predicts that (a) slower mode-switching should be associated with learning of more temporally extended input features and (b) disruption of switching should impair the integration of new information with prior information.