Statistically optimal perception and learning: from behavior to neural representations.
Statistically optimal perception and learning: from behavior to neural representations.
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DOI：
10.1016/j.tics.2010.01.003
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发表时间：
2010-03
影响因子：
19.9
通讯作者：
Lengyel, Mate
中科院分区：
文献类型：
--
作者：
Fiser, Jozsef;Berkes, Pietro;Orban, Gergo;Lengyel, Mate
人类的感知最近被描述为基于有噪声且模糊的感官输入的统计推断。此外，已经确定了合适的不确定性神经表征，这些表征可能是这种概率计算的基础。在这篇综述中，我们认为学习感官环境的内部模型是同一统计推断过程的另一个关键方面，因此感知和学习需要共同考虑。我们回顾了人类和动物中统计最优学习的证据，并根据其支持统计最优学习的潜力重新评估了不确定性的可能神经表征。我们提出，自发活动在这种表征中可以具有功能性作用，从而形成一个关于大脑皮层如何表征信息和不确定性的基于采样的新框架。
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.
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