While modern LLMs are a far cry from biological synapses, I do find it fascinating that if you take the highly reciprocal data of a biological connectome and unroll it into a DAG, you suddenly see motifs popping up that look similar to what we find in AI. I found this both looking at temporal unrolling of RNNs or mapping layer activation weights of a Transformer. Totally agree though, the current LLM architecture itself is driven by the need to shove all of this nicely into parallelized compute hardware.