Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?. (arXiv:2208.12564v1 [cs.NE])

It is generally assumed that the brain uses something akin to sparse
distributed representations. These representations, however, are
high-dimensional and consequently they affect classification performance of
traditional Machine Learning models due to "the curse of dimensionality". In
tasks for which there is a vast amount of labeled data, Deep Networks seem to
solve this issue with many layers and a non-Hebbian backpropagation algorithm.
The brain, however, seems to be able to solve the problem with few layers. In
this work, …

arxiv data dimensionality distributed distributed data learning the curse of dimensionality