According to brain and machine learning expert, Jeff Hawkins, goal-directed behavior is the holy grail of intelligence and robotics. He believes that the best way to solve the intelligence puzzle is to emulate the brain. Hawkins is right, of course. There is no question that we can learn everything we need to know about intelligence by studying the brain. The only problem is that some of the answers are so deeply buried in an ocean of complexity that a hundred years of painstaking research could not uncover them. In this multi-part article, I will describe some of the amazing secrets of the brain before revealing the surprising source of my knowledge (no, it's not the brain, sorry).
Liars and Thieves
Let me come right out with a bold statement: nobody can rightfully claim to understand the brain’s perceptual learning mechanism without also knowing exactly what the brain does during sleep and why. Sure, we know what neuroscientists and psychologists have told us, that the brain uses sleep to consolidate recent memories, whatever that means. Unfortunately, that is pretty much the extent of their knowledge on the subject. Hawkins doesn't know either, although he should. That is, assuming he wants to stay in this business. It turns out that the brain performs at least two essential functions while we are asleep: it purges liars (bad predictors) from sequence memory and eliminates thieves (redundant connections) from pattern memory.
Note: I will explain my choice of the liars and thieves metaphors in an upcoming post.Without these frequent purges, the brain would get confused and eventually stop working. But why is that, you ask? That, my astute and inquisitive friend, is one of the secrets of the holy grail, which is why you must read the rest of the article. But before I can answer your question, I must first say a few things about how memory is organized.
I did not always think so but the brain has two types of hierarchical memories: pattern memory and sequence memory. My original objection was that a pattern hierarchy cannot do invariant object recognition. That was before I realized that it doesn't have to; that's the purpose of sequence memory. Pattern memory is a hierarchy of pattern detectors that send their output signals directly to sequence memory. A pattern is a transient group of sensory signals that occur together often and a pattern detector or neuron is best viewed as a complex event sensor. Pattern detectors (red-filled circles) can have an indefinite number of inputs.
A hierarchy makes sense for several reasons. First, it gives us a very compact storage structure because of the inherent reuse of lower level patterns. Second, and just as importantly, it provides a way to automatically limit the boundaries of patterns. This, in turn, makes it possible to discover all possible patterns in the sensory space. I'll have more to say on this later.
A peculiar but critical aspect of pattern memory is that the time it takes an incoming signal to propagate through the hierarchy must be very fast. The cortex uses electric synapses to do this. The end result is that signal propagation through the hierarchy appears instantaneous to the rest of the brain. And the reason for this has to do with timing integrity. For instance, if a high level neuron (A) fires, all the pattern neurons in the branch below A in the hierarchy are assumed to have fired concurrently with A.
It would be accurate to say that sequence memory is the seat of intelligence. It is used for many functions such as recollection, prediction, attention, invariant object recognition, reasoning, goal-directed motor behavior and adaptation. Sequence memory contains sequences of patterns organized hierarchically just like pattern memory. Note that, in the diagram below, the pattern hierarchy is shown as a single flat layer (red circles). This is because sequence memory (yellow circles) does not see pattern memory as a hierarchy. That is to say, the system must act as if sensory signals could travel through pattern hierarchy instantaneously. Otherwise, pattern detection timing would be askew.
One of the more interesting design characteristics of sequence memory is that a sequence detector has a maximum of seven nodes or inputs. Why seven? For one, it explains the capacity of what psychologists call short-term or working memory. Second, it is a compromise that aims to minimize energy usage while maximizing the breadth of focus. As it turns out, the brain can focus on only one branch of sequence memory at a time. A branch should be seen as a grouping mechanism that represents a single object or concept. No need to look any further. The branch is the mechanism of both attention and invariant object recognition.
What is even more interesting from the point of view of invariant object recognition is that multiple sequences may and do share patterns. In fact, every complex recognized object in memory consists of multiple, tightly intertwined sequences. This will become clearer later.
In Part II, I will explain how learning occurs in pattern memory and how to catch a thief.
The Holy Grail of Robotics
Raiders of the Holy Grail
Jeff Hawkins Is Close to Something Big