不确定世界的神经标度律
Neural scaling laws for an uncertain world
摘要：
自主神经系统必须在广泛的新环境中有效地处理信息，这些环境可能具有非常不同的统计特性。我们考虑的问题，如何最佳地分布受体沿一维连续与以下设计原则一致。首先，世界的神经表征应该遵守神经不确定性原则-尽可能少地对世界的统计结构做出假设。其次，神经表征应该尽可能多地传达关于具有不同统计数据的环境的等效信息。这些论点的结果类似于视觉系统的结构，并为行为韦伯-费希纳定律提供了一个自然的解释，这是心理学的一个基础结果。由于推导是非常普遍的，这表明，类似的比例关系不仅应该在感觉连续统中观察到，而且在“认知”一维量（如时间或数字）的神经表征中也应该观察到。
英文摘要：
Autonomous neural systems must efficiently process information in a wide range of novel environments, which may have very different statistical properties. We consider the problem of how to optimally distribute receptors along a one-dimensional continuum consistent with the following design principles. First, neural representations of the world should obey a neural uncertainty principle---making as few assumptions as possible about the statistical structure of the world. Second, neural representations should convey, as much as possible, equivalent information about environments with different statistics. The results of these arguments resemble the structure of the visual system and provide a natural explanation of the behavioral Weber-Fechner law, a foundational result in psychology. Because the derivation is extremely general, this suggests that similar scaling relationships should be observed not only in sensory continua, but also in neural representations of ``cognitive' one-dimensional quantities such as time or numerosity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
