Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, United States.
Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, United States.
Neuroscience. 2018 Oct 1;389:161-174. doi: 10.1016/j.neuroscience.2018.04.030. Epub 2018 May 2.
The world is richly structured on multiple spatiotemporal scales. In order to represent spatial structure, many machine-learning models repeat a set of basic operations at each layer of a hierarchical architecture. These iterated spatial operations - including pooling, normalization and pattern completion - enable these systems to recognize and predict spatial structure, while robust to changes in the spatial scale, contrast and noisiness of the input signal. Because our brains also process temporal information that is rich and occurs across multiple time scales, might the brain employ an analogous set of operations for temporal information processing? Here we define a candidate set of temporal operations, and we review evidence that they are implemented in the mammalian cerebral cortex in a hierarchical manner. We conclude that multiple consecutive stages of cortical processing can be understood to perform temporal pooling, temporal normalization and temporal pattern completion.
世界在多个时空尺度上具有丰富的结构。为了表示空间结构，许多机器学习模型在层次结构的每一层重复一组基本操作。这些迭代的空间操作——包括池化、归一化和模式完成——使这些系统能够识别和预测空间结构，同时对输入信号的空间尺度、对比度和噪声具有鲁棒性。由于我们的大脑还处理丰富的、跨越多个时间尺度的时间信息，大脑是否会采用类似的操作来处理时间信息？在这里，我们定义了一组候选的时间操作，并回顾了证据，证明它们在哺乳动物大脑皮层中以层次化的方式被执行。我们的结论是，皮质处理的多个连续阶段可以被理解为执行时间池化、时间归一化和时间模式完成。
