Prediction

 

Every region of an HTM stores sequences of patterns. By matching stored sequences with current input, a region forms a prediction about what inputs will likely arrive next. HTM regions actually store transitions between sparse distributed representations. In some instances the transitions can look like a linear sequence, such as the notes in a melody, but in the general case many possible

future inputs may be predicted at the same time. An HTM region will make different predictions based on context that might stretch back far in time. The majority of memory in an HTM is dedicated to sequence memory, or storing transitions

between spatial patterns.

 

Following are some key properties of HTM prediction.

 

Prediction is continuous.

Without being conscious of it, you are constantly predicting. HTMs do the same. When listening to a song, you are predicting the next note. When walking down the

stairs, you are predicting when your foot will touch the next step. When watching a baseball pitcher throw, you are predicting that the ball will come near the batter. In

an HTM region, prediction and inference are almost the same thing. Prediction is not a separate step but integral to the way an HTM region works.