In Part I, I gave a brief description of the brain's memory architecture. In this post, I explain how the brain does pattern learning and catches "thieves" in its sleep.
Winner-Takes-All vs the Bayesian Brain
Although it feels like I am preaching in the wilderness, I have been railing against the use of Bayesian statistics in machine learning for some time now. The idea that the brain reasons or recognizes objects by juggling statistics is ridiculous when you think about it. The brain actually abhors uncertainty and goes to great lengths to eliminate it. As computer scientist Judea Pearl put it not too long ago, "people are not probability thinkers but cause-effect thinkers."
Even though it is continually bombarded with noisy and incomplete sensory data, internally, the brain is strictly deterministic. It uses a winner-takes-all mechanism in which sequences compete to fire and the winner is the one with the most hits. Once a winner is determined, the other competitors are immediately suppressed. The winning sequence is assumed to be perfect. To repeat, the brain is not a probability thinker. It learns every pattern and sequence that it can learn, anything that is more than mere random chance. Then it lets them compete for attention. Read The Myth of the Bayesian Brain for more on this topic.
The job of the pattern learner is to discover as many unique patterns in the sensory space as possible. Pattern learning consists of randomly connecting sensory inputs to pattern neurons and checking to see if they fire concurrently. However, keep in mind that a pattern neuron will fire when a majority of its input signals arrive concurrently.The pattern learning rule is simple and powerful but it suffers from a major flaw: it imposes no restrictions or boundaries on the growth of a pattern. Without proper boundaries, patterns become more and more complex and the simpler ones eventually disappear, crippling the system. Obviously, we need a way to prevent a pattern neuron from acquiring more complexity than its level within the hierarchy requires. The solution to the boundary problem consists of enforcing the boundary rule:
Catching Thieves During Sleep
In order to become permanent, an input connection must contribute to the firing of its pattern neuron at least X times in a row.X is a number that depends on the desired learning speed of the system. In the human brain, X equals 10. With this rule, the brain can quickly find patterns in the sensory space. It is based on the observation that sensory signals are not always imperfect. Every once in a while, even if for a brief interval, they are perfect. This perfection is captured in pattern memory.
Catching Thieves During Sleep
A pattern may not have duplicate sensory input connections.Here is another way of putting it: a sensory signal may not arrive at a pattern neuron via more than one path. For example, in the illustration below, pattern neuron A behaves as if it were connected directly to sensors a, b, c, d, and e.
The power of the boundary rule is betrayed by its simplicity. It prevents runaway pattern growth while facilitating the discovery of every possible unique pattern in the sensory space. It is indispensable to pattern learning and works for any type of sensory patterns, not just visual.
Note: As far as I know, the boundary rule is not in any books. Please make copies of this page on your computer. This is intended to serve as "prior art" in the public domain, i.e., it cannot be patented. :-DThe brain cannot eliminate thieves while it is awake because it must test fire all untested connections. This could cause problems during waking hours. This is one of the reasons that sleep is so important. An intelligent machine, by contrast, is not so limited. During learning, a computer program can examine a branch on the fly to see if a new connection is a thief.
In Part III, I will show how learning occurs in sequence memory.
The Myth of the Bayesian Brain
The Holy Grail of Robotics
Raiders of the Holy Grail
Jeff Hawkins Is Close to Something Big