One key "architectural" difference seems to be the asynchronous or event driven nature of the brain vs. the "full computational" approach of most DNN architectu

One key "architectural" difference seems to be the asynchronous or event driven nature of the brain vs. the "full computational" approach of most DNN architectures currently en vogue. There is some research into spiking neural networks, however, there seems to be no relevance of them yet with respect to most current problems (e.g. NLP, visuals). Regarding the use case AGI (assuming we would want to model a complete brain to do that) my quick over the envelope calculation is: Given that the brain has 100 billion neurons with about 5000 connections each that contain state (ignoring the various neurotransmitter side effects) at half precision we require roughly 1 PB of memory for it. Add some factor <10 if you want to include the routing information to make the connections dynamic. Regarding computational capacity of a event driven architecture for AGI based on the brain: Assuming each neuron fires on average at 100 Hz on each connection that would amount to 50 PetaFlops. Effectively, this number could be lower, if that average firing rate and connection utilization goes down. So when looking at the current supercomputer list, there should be some machines around that would be able to do such calculations assuming we have tools to model the architecture.