Another, subtler way an HTM exploits time is how it decides what to learn. All of its parts learn on their own, without a programmer or anyone else telling the neurons what to do. It is tempting for us to try to fill such a coordinating role by deciding in advance what a node should do, for instance by saying, "Node A will learn to recognize eyes and ears, and node B will learn noses and fur." However, that approach does not work. As nodes learn, they change their output-which affects the input to other nodes. Because memory in an HTM is dynamic, it is not possible to decide in advance what a node should learn.

So how does a node know what to learn? This is where time plays a critical role and is one of the unique aspects of HTM. Patterns that occur close together in time generally have a common cause. For instance, when we hear a sequence of notes over and over, we learn to recognize them as a single thing, a melody. We do the same with visual and tactile patterns. Seeing a dog moving in front of us, for example, is what teaches us that a left-facing dog is actually the same as a right-facing dog, in spite of the fact that the actual information on the retina is different from moment to moment. HTM nodes learn similarly; they use time as a teacher. In fact, the only way to train an HTM is with input that changes over time. How that is done is the most challenging part of HTM theory and practice.

Because HTMs, like humans, can recognize spatial patterns such as a static picture, you might think that time is not essential. Not so. Strange though it may seem, we cannot learn to recognize pictures without first training on moving images. You can see why in your own behavior. When you are confronted with a new and confusing object, you pick it up and move it about in front of your eyes. You look at it from different directions and top and bottom. As the object moves and the patterns on your retina change, your brain assumes that the unknown object is not changing. Nodes in an HTM assemble differing input patterns together under the assumption that two patterns that repeatedly occur close in time are likely to share a common cause. Time is the teacher.

The final word in HTM is "memory." This attribute distinguishes HTMs from systems that are programmed. Most of the effort in building an HTM-based system is spent in training the system by exposing it to sensory data, not in writing code or configuring the network. Some people assume memory means a single remembered instance, such as "what I ate for lunch." Others associate memory with computer memory. In the case of HTM, it is neither. HTMs are hierarchical, dynamic, memory systems.

What makes HTM different from other approaches to machine learning? HTMs are unique not because we have discovered some new and miraculous concept. HTM combines the best of several existing techniques, with a few twists thrown in. For example, hierarchical representations exist in a technique called Hierarchical Hidden Markov Models. However, the hierarchies used in HHMMs are simpler than those in HTM. Even though HHMMs can learn complex temporal patterns, they do not handle spatial variation well. It is as if you could learn melodies but not be able to recognize them when played in a different key. Still, the similarity between HTM and other approaches is a good sign: it means that other people have reached similar conclusions. A detailed comparison to other techniques is available on Numenta's Web site.

Another unique aspect of HTM is that it is a biological model as well as a mathematical model. The mapping between HTM and the detailed anatomy of the neocortex is deep. As far as we know, no other model comes close to HTM's level of biological accuracy. The mapping is so good that we still look to neuroanatomy and physiology for direction whenever we encounter a theoretical or technical problem.

Finally, HTMs work. "If we really understand a system we will be able to build it," said Carver Mead, the famous Caltech electrical engineer. "Conversely, we can be sure that we do not fully understand the system until we have synthesized and demonstrated a working model." We have built and tested enough HTMs of sufficient complexity to know that they work. They work on at least some difficult and useful problems, such as handling distortion and variances in visual images. Thus we can identify dogs as such, in simple images, whether they face right or left, are big or small, are seen from the front or the rear, and even in grainy or partially occluded images.