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.

本研究旨在探究如何最优地分布受体，提高自主神经系统在不同统计情况下的处理信息的能力，并在视觉系统的基础上解释行为型魏伯 - 费希纳定律。