  In the decade since Jeff Hawkins proposed Hierarchical Temporal Memory (HTM)
as a model of neocortical computation, the theory and the algorithms have
evolved dramatically. This paper presents a detailed description of HTM's
Cortical Learning Algorithm (CLA), including for the first time a rigorous
mathematical formulation of all aspects of the computations. Prediction
Assisted CLA (paCLA), a refinement of the CLA is presented, which is both
closer to the neuroscience and adds significantly to the computational power.
Finally, we summarise the key functions of neocortex which are expressed in
paCLA implementations.
