  Efforts at understanding the computational processes in the brain have met
with limited success, despite their importance and potential uses in building
intelligent machines. We propose a simple new model which draws on recent
findings in Neuroscience and the Applied Mathematics of interacting Dynamical
Systems. The Feynman Machine is a Universal Computer for Dynamical Systems,
analogous to the Turing Machine for symbolic computing, but with several
important differences. We demonstrate that networks and hierarchies of simple
interacting Dynamical Systems, each adaptively learning to forecast its
evolution, are capable of automatically building sensorimotor models of the
external and internal world. We identify such networks in mammalian neocortex,
and show how existing theories of cortical computation combine with our model
to explain the power and flexibility of mammalian intelligence. These findings
lead directly to new architectures for machine intelligence. A suite of
software implementations has been built based on these principles, and applied
to a number of spatiotemporal learning tasks.
