The HTM sequence memory model. (A) The cortex is organized into six cellular layers. Each cellular layer consists of a set of minicolumns, with each minicolumn containing multiple cells. (B) An HTM neuron (left) has three distinct dendritic integration zones, corresponding to different parts of the dendritic tree of pyramidal neurons (right). An HTM neuron models dendrites and NMDA spikes as an array of coincident detectors each with a set of synapses. The coactivation of a set of synapses on a distal dendrite will cause an NMDA spike and depolarize the soma (predicted state). (C, D) Learning high-order Markov sequences with shared sub-sequences (ABCD versus XBCY). Each sequence element invokes a sparse set of minicolumns due to intercolumn inhibition. (C) Prior to learning the sequences all the cells in a minicolumn become active. (D) After learning, cells that are depolarized through lateral connections become active faster and prevent other cells in the same column from firing through intracolumn inhibition. The model maintains two simultaneous representations: one at the minicolumn level representing the current feedforward input and the other at the individual cell level representing the context of the input. Because different cells respond to C in the two sequences (C’ and C”), they can invoke the correct high-order prediction of either D or Y.