Neural scaling laws for an uncertain world
Files
Accepted manuscript
Date
2018
Authors
Howard, Marc W.
Shankar, Karthik H.
Version
Accepted manuscript
OA Version
Citation
M.W. Howard, Karthik H Shankar. 2018. "Neural scaling laws for an uncertain world." Psychological Review, Volume 125, Issue 1, pp. 47 - 58. https://doi.org/10.1037/rev0000081
Abstract
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 WeberFechner 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.