  A fundamental feature of human intelligence is that we accumulate and
transfer knowledge as a society and across generations. We describe here a
network architecture for the human brain that may support this feature and
suggest that two key innovations were the ability to consider an offline model
of the world, and the use of language to record and communicate knowledge
within this model. We propose that these two innovations, together with
pre-existing mechanisms for associative learning, allowed us to develop a
conceptually simple associative network that operates like a reservoir of
attractors and can learn in a rapid, flexible, and robust manner. We
hypothesize that explicit human intelligence is based primarily on this type of
network, which works in conjunction with older and likely more complex deep
networks that perform sensory, motor, and other implicit forms of processing.
