Kurzweil's (2012) pattern recognition theory of mind (PRTM) is based on a certain model of the neocortex, which I shall outline here. I believe that the model is bound to be correct, for two reasons. One is the immensely graphic and detailed experimental data we now have about the structure of the brain, as I outlined in the previous post. The other reason is the spectacular success already achieved in creating an artificial brain based on the PRTM; the success of, for example, IBM's Watson (cf. Part 118) is proof of that.
The human neocortex is an essentially 2-dimensional, ~2.5-mm thick, structure, comprising of six layers. The layers are numbered from I (the outermost layer) to VI (cf. Part 122).
The PRTM says, in essence, that all of the many wonders of the neocortex can be reduced to a single type of thought process, involving hierarchical thinking. This is lent credence to by the structure of the neocortex itself. Its fundamental structure and function has an extraordinarily high degree of uniformity, a la Vernon Mountcastle (cf. Part 124). And this structure is hierarchical in nature.
Mountcastle also postulated the existence of cortical columns along the thickness of the neocortex. The six layers and the cortical columns in them together imply the existence of a grid structure, which has been confirmed by experiment (cf. Part 124).
Kurzweil (2012) hypothesizes that the basic uniform unit of action in the entire neocortex is the so-called pattern recognizer (PR); it is the fundamental component of the neocortex. Deviating a bit from Mountcastle's model, Kurzweil stipulates that the PRs are not separated by specific physical boundaries; rather they are placed closely one to the next in an interwoven fashion. A cortical column is simply an aggregate of a large number of PRs.
The PRs wire themselves to one another throughout the course of a lifetime. Therefore the elaborate connectivity between modules that is there in the neocortex is not specified much by the genetic code; rather it gets created to embody the patterns we actually learn over time.
Kurzweil estimates that there are ~500,000 cortical columns in the human neocortex, each being ~0.5 mm wide and ~2.5 mm long. Each contains ~60,000 neurons. Since each PR within a cortical column contains ~100 neurons, it follows that there are ~500,000 x 60,000 / 100 or ~300 million PRs in our neocortex.
How many patterns can the human neocortex store? With as many as 300 million PRs available, our brain can indulge in a huge amount of redundancy, resulting in our fantastic pattern-recognition capability, which is far in excess of what any computer system has been able to attain so far. [However, let us also remind ourselves that computer processes are millions of times faster than the electrochemical processes that occur in our brains.]
Here is an example of the redundancy with which our brain stores patterns. The face of a loved one is not stored just once, but thousands of times. Some are just repetitions, but most are different perspectives of the face, differing in lighting, facial expressions, etc. And none of these repeated patterns are stored as 2-dimensional arrays of pixels. They are stored as 1-dimensional lists of features, but hierarchically: The constituent elements of a pattern are themselves patterns, and so on.
Even our procedures and actions comprise patterns, and are likewise stored in the neocortex.
Kurzweil's estimate of the total capacity of the human neocortex is on the order of low hundreds of millions of patterns, which is similar to the number of PRs, namely ~300 million.
The structure of a pattern
The PRTM says that patterns are recognized by pattern-recognition modules in the neocortex, and that the patterns and the modules are organized in hierarchies. When a pattern is recognized, there are three parts to this process. To make the description concrete, let us take the example of an APPLE, and also the word 'APPLE' we use for referring to this physical entity.
Part one is the input, consisting of the lower-level patterns that compose the main pattern. The descriptions of each of these lower-level patterns do not need to be repeated for each higher-level pattern that references them. The letter 'A' appears in the pattern for the word APPLE and also in a large number of other words. Each of these patterns need not repeat a description for the pattern of A, but can use a common description stored somewhere. All that is required is a neural connection to that location. There is an axon from the 'A' pattern recognizer that connects to multiple dendrites, one for each word that uses 'A'.
Part two of each pattern is the name of the pattern. This 'name' is simply the axon that emerges from each pattern processor. When the axon fires, its corresponding pattern has been recognized. It is as if the pattern recognizer is shouting: 'Hey guys, I just saw the written word "apple"'.
Part three of each pattern is the set of higher-level patterns that it, in turn, is part of. For the letter 'A' it is all words that include 'A'.
For the example of apple the object and apple the word, just like the hierarchy for the storage and recognition of the word 'apple', another part of the cortex has a hierarchy of pattern-recognizers processing the actual images of objects. If you are looking at an apple, the corresponding pattern recognizer will fire its axon, saying in effect: 'Hey guys, I just saw an actual apple'. Similarly, if somebody utters the word 'apple', the corresponding auditory pattern-recognizer will be triggered.
Information flows down the conceptual hierarchy as well as up. To quote Kurzweil (2012): 'If, for example, we are reading from left to right and have already seen and recognized the letters "A", "P", "P", "L, " the "APPLE" recognizer will predict that it is likely to see an "E" in the next position. It will send a signal down to the "E" recognizer saying, in effect, "Please be aware that there is a high likelihood that you will see your 'E' pattern very soon, so be on the lookout for it"'. The 'E' recognizer then adjusts (lowers) its threshold or action potential for the firing of the neuron which would potentially declare that 'E' has been seen. Even if an incomplete or smudged image of 'E' appears, it would be recognized correctly because it was expected.
This prediction feature is one of the primary reasons why we have a neocortex at all. Our brain is making predictions all the time, and at all levels of abstraction.
More on this next time.