Learning

An HTM region learns about its world by finding patterns and then sequences of patterns in sensory data. The region does not “know” what its inputs represent; it works in a purely statistical realm. It looks for combinations of input bits that occur together often, which we call spatial patterns. It then looks for how these spatial patterns appear in sequence over time, which we call temporal patterns or sequences.

If the input to the region represents environmental sensors on a building, the region might discover that certain combinations of temperature and humidity on the north side of the building occur often and that different combinations occur on the south side of the building. Then it might learn that sequences of these combinations occur as each day passes.

If the input to a region represented information related to purchases within a store, the HTM region might discover that certain types of articles are purchased on weekends, or that when the weather is cold certain price ranges are favored in the evening. Then it might learn that different individuals follow similar sequential patterns in their purchases.

A single HTM region has limited learning capability. A region automatically adjusts what it learns based on how much memory it has and the complexity of the input it receives. The spatial patterns learned by a region will necessarily become simpler if the memory allocated to a region is reduced. Or the spatial patterns learned can become more complex if the allocated memory is increased. If the learned spatial patterns in a region are simple, then a hierarchy of regions may be needed to understand complex images. We see this characteristic in the human vision system where the neocortical region receiving input from the retina learns spatial patterns for small parts of the visual space. Only after several levels of hierarchy do spatial patterns combine and represent most or all of the visual space.

Like a biological system, the learning algorithms in an HTM region are capable of

“on-line learning”, i.e. they continually learn from each new input. There isn’t a need

for a learning phase separate from an inference phase, though inference improves after additional learning. As the patterns in the input change, the HTM region will gradually change, too.

After initial training, an HTM can continue to learn or, alternatively, learning can be disabled after the training phase. Another option is to turn off learning only at the lowest levels of the hierarchy but continue to learn at the higher levels. Once an HTM has learned the basic statistical structure of its world, most new learning occurs in the upper levels of the hierarchy. If an HTM is exposed to new patterns that have previously unseen low-level structure, it will take longer for the HTM to learn these new patterns. We see this trait in humans. Learning new words in a language you already know is relatively easy. However, if you try to learn new words from a foreign language with unfamiliar sounds, you’ll find it much harder because you don’t already know the low level sounds.

Simply discovering patterns is a potentially valuable capability. Understanding the high-level patterns in market fluctuations, disease, weather, manufacturing yield, or failures of complex systems, such as power grids, is valuable in itself. Even so, learning spatial and temporal patterns is mostly a precursor to inference and prediction.