Temporal pooler details

 

Here we enumerate the steps performed by the temporal pooler. We start where

the spatial pooler left off, with a set of active columns representing the feed-forward input.

 

1) For each active column, check for cells in the column that are in a predictive state, and activate them. If no cells are in a predictive state, activate all the cells in the column. The resulting set of active cells is the representation of the input in the context of prior input.

 

2) For every dendrite segment on every cell in the region, count how many established synapses are connected to active cells. If the number exceeds a threshold, that dendrite segment is marked as active. Cells with active dendrite segments are put in the predictive state unless they are already active due to feed- forward input. Cells with no active dendrites and not active due to bottom-up input become or remain inactive. The collection of cells now in the predictive state is the prediction of the region.

 

3) When a dendrite segment becomes active, modify the permanence values of all

the synapses associated with the segment. For every potential synapse on the active dendrite segment, increase the permanence of those synapses that are connected to

active cells and decrement the permanence of those synapses connected to inactive cells. These changes to synapse permanence are marked as temporary.

 

This modifies the synapses on segments that are already trained sufficiently to make the segment active, and thus lead to a prediction. However, we always want to extend predictions further back in time if possible. Thus, we pick a second dendrite segment on the same cell to train. For the second segment we choose the one that best matches the state of the system in the previous time step. For this segment, using the state of the system in the previous time step, increase the permanence of those synapses that are connected to active cells and decrement the permanence of those synapses connected to inactive cells. These changes to synapse permanence are marked as temporary.

 

4) Whenever a cell switches from being inactive to active due to feed-forward input, we traverse each potential synapse associated with the cell and remove any temporary marks. Thus we update the permanence of synapses only if they correctly predicted the feed-forward activation of the cell.

 

5) When a cell switches from either active state to inactive, undo any permanence changes marked as temporary for each potential synapse on this cell. We don’t want to strengthen the permanence of synapses that incorrectly predicted the feed- forward activation of a cell.

 

Note that only cells that are active due to feed-forward input propagate activity within the region, otherwise predictions would lead to further predictions. But all the active cells (feed-forward and predictive) form the output of a region and propagate to the next region in the hierarchy.