A picture of two adjacent layers of GLOM for a single location (i.e., part of a single column). During the forward pass, the embedding vector at level L receives input from the level L-1 embedding vector in the previous layer via a multilayer, bottom-up neural net. It also receives input from the level L+1 embedding in the previous layer via a multilayer, top-down neural net. The dependence on level L+1 in the previous layer implements top-down effects during the forward pass. The level L embedding in layer $t+1$ also depends on the level L embedding in layer $t$ and an attention-weighted sum of the level L embeddings at other nearby locations in layer $t$⁠. These within-level interactions are not shown.