Form a representation of the input in the context of previous inputs

The next function performed by a region is to convert the columnar representation of the input into a new representation that includes state, or context, from the past. The new representation is formed by activating a subset of the cells within each column, typically only one cell per column (Figure 2.2).

 

Consider hearing two spoken sentences, “I ate a pear” and “I have eight pears”. The words “ate” and “eight” are homonyms; they sound identical. We can be certain that at some point in the brain there are neurons that respond identically to the spoken words “ate” and “eight”. After all, identical sounds are entering the ear. However, we also can be certain that at another point in the brain the neurons that

respond to this input are different, in different contexts. The representations for the

sound “ate” will be different when you hear “I ate” vs. “I have eight”. Imagine that you have memorized the two sentences “I ate a pear” and “I have eight pears”.

Hearing “I ate…” leads to a different prediction than “I have eight…”. There must be

different internal representations after hearing “I ate” and “I have eight”.

 

This principle of encoding an input differently in different contexts is a universal feature of perception and action and is one of the most important functions of an HTM region. It is hard to overemphasize the importance of this capability.

 

Each column in an HTM region consists of multiple cells. All cells in a column get the same feed-forward input. Each cell in a column can be active or not active. By selecting different active cells in each active column, we can represent the exact

same input differently in different contexts. A specific example might help. Say every column has 4 cells and the representation of every input consists of 100 active columns. If only one cell per column is active at a time, we have 4^100 ways of representing the exact same input. The same input will always result in the same

100 columns being active, but in different contexts different cells in those columns will be active. Now we can represent the same input in a very large number of

contexts, but how unique will those different representations be? Nearly all

randomly chosen pairs of the 4^100 possible patterns will overlap by about 25 cells. Thus two representations of a particular input in different contexts will have about

25 cells in common and 75 cells that are different, making them easily distinguishable.

The general rule used by an HTM region is the following. When a column becomes active, it looks at all the cells in the column. If one or more cells in the column are already in the predictive state, only those cells become active. If no cells in the column are in the predictive state, then all the cells become active. You can think of it this way, if an input pattern is expected then the system confirms that expectation by activating only the cells in the predictive state. If the input pattern is unexpected then the system activates all cells in the column as if to say “the input occurred unexpectedly so all possible interpretations are valid”.

 

If there is no prior state, and therefore no context and prediction, all the cells in a column will become active when the column becomes active. This scenario is similar to hearing the first note in a song. Without context you usually can’t predict what will happen next; all options are available. If there is prior state but the input does not match what is expected, all the cells in the active column will become active. This determination is done on a column by column basis so a predictive match or mismatch is never an “all-or-nothing” event.

 

Figure 2.2: By activating a subset of cells in each column, an HTM region can represent the same input in many different contexts. Columns only activate predicted cells. Columns with no predicted cells activate all the cells in the column. The figure shows some columns with one cell active and some columns with all cells active.

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As mentioned in the terminology section above, HTM cells can be in one of three states. If a cell is active due to feed-forward input we just use the term “active”. If the cell is active due to lateral connections to other nearby cells we say it is in the “predictive state” (Figure 2.3).