  Reverse engineering the brain is proving difficult, perhaps impossible. While
many believe that this is just a matter of time and effort, a different
approach might help. Here, we describe a very simple idea which explains the
power of the brain as well as its structure, exploiting complex dynamics rather
than abstracting it away. Just as a Turing Machine is a Universal Digital
Computer operating in a world of symbols, we propose that the brain is a
Universal Dynamical Systems Modeller, evolved bottom-up (itself using nested
networks of interconnected, self-organised dynamical systems) to prosper in a
world of dynamical systems.
  Recent progress in Applied Mathematics has produced startling evidence of
what happens when abstract Dynamical Systems interact. Key latent information
describing system A can be extracted by system B from very simple signals, and
signals can be used by one system to control and manipulate others. Using these
facts, we show how a region of the neocortex uses its dynamics to intrinsically
"compute" about the external and internal world.
  Building on an existing "static" model of cortical computation (Hawkins'
Hierarchical Temporal Memory - HTM), we describe how a region of neocortex can
be viewed as a network of components which together form a Dynamical Systems
modelling module, connected via sensory and motor pathways to the external
world, and forming part of a larger dynamical network in the brain.
  Empirical modelling and simulations of Dynamical HTM are possible with simple
extensions and combinations of currently existing open source software. We list
a number of relevant projects.
