Ok, here is a single-centroid version of backprop, modeled after dendritic tree:
[2211.11378] Learning on tree architectures outperforms a convolutional feedforward network,
Is Brain Learning Weaker Than Artificial Intelligence? - Neuroscience News
“We’ve shown that efficient learning on an artificial tree architecture, where each weight has a single route to an output unit, can achieve better classification success rates than previously achieved by DL architectures consisting of more layers and filters. This finding paves the way for efficient, biologically-inspired new AI hardware and algorithms,” said Prof. Ido Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.