Why do we tend to obsessiveness categorize things? Every sensory item, every abstract idea, everything fits into one or more categories. Try it yourself, try to think of something that does not fit into a category. You cant!
I propose that a category trie is the fundamental data structure of the brain for both conscious and subconscious memories. These memories include everything from personalities(big memory) to senones(the building blocks of phonemes- small memories).
In computer science a trie is an extremely efficient data structure for storing and accessing data. When the trie is brought from the a RAM/CPU architecture to a neural architecture magic happens. All of a sudden the trie is no longer just storage structure but a highly adaptable, fast, efficient, highly parallel processing machine!
To demonstrate lets use speech recognition as an example of the effectiveness of trie categorization. Note that speech recognition is a particularly easy example, but the argument holds true for any type of object recognition (visual, oral, etc) as well as for decision making(to be explained in future posts).
We know the human ears works. It is therefore it is a good reference point for this discussion. Sound comes to the brain through the ears where the spiral ganlion perform the equivalent of a Fourier Transform on the incoming sound waves. The brains auditory cortex thus receives electrical signals as a function of frequency.
When the brain recognizes a sound, it is essentially taking a combination of pitch, volume, and timing and turning it into an image/word in the consciousness. For example if we take a bird’s tweet and shift it from 1khz to 100hz it would be totally unrecognizable. Or if we took the same tweet and extended the silent parts to be 40 seconds pauses it would also be unrecognizable. From this we see the importance of both temporal and frequency information.
So how does this all relate to neurons?
The beauty of neurons is there sensitivity to temporal information. A neuron will only fire if it receives a given stimulation within a given time period. If the time period is too long or the stimulation too weak then the neuron does not fire. The implications of this are that neurons are built to process temporal sequential information.
Back to our speech recognition example. The sound goes through the Fourier Transform is translated to electrical activity then hits the root level of the trie. The roots of the trie corresponding to the incoming frequencies then fire exciting the second level of the network. Each level of the network acts as a filter, categorizing the data from the above level and sending it on.
Look at the trie diagram above. For simplicity lets pretend that instead of representing frequencies the roots of the trie represented letters. As the letter “i” comes in from the ears, the “i” neuron fires exciting the “in” neuron. The “in” neuron however will not fire until it has received a signal from both the “i” neuron and then the “n” neuron. If another “n” comes in from the ears then that will be sent to “in” and “inn” will fire.
But what if “in” fires and then another “n” comes from the ears? Now we have fired “in” and “inn” and created quite a mess! This is where the inhibitory neurons come to save the day. But before we go there notice the incredible thing that is happening.
The neurons that store data on how phonemes sound are the same neurons that process the data for the incoming sounds. In a neural network the storage sorts!