TR: What do you want to add to the tremendous amount of neuroscience research that’s already being done?
HAWKINS: In reading about the brain, I found that what was conspicuously absent was any sort of overarching theory to explain it. I noticed brain research was paying little attention to certain things. For instance, look at the cerebral cortex, or neocortex. It’s essentially a big sheet of neurons several millimeters thick. Although there are areas dedicated to vision, speech, touch and motor output, it’s a remarkably uniform structure-the areas that deal with vision are almost identical to those dealing with hearing. This similarity implies that the same basic mechanism underlies all sensory processing. This is a remarkable finding-yet it has been generally ignored.
TR: Why is this discovery so important?
HAWKINS: Because it helps explain how the brain processes all the information it receives. The major inputs to the brain are the optic nerve, the spinal cord-touch, if you will-and the auditory nerve. However, there’s really only one thing coming into the brain: patterns of neural firings. Now think about what these neural patterns are really like. First, your eyes are moving all the time. While you’re looking at my face, your eyes are doing these little dance movements called saccades. Combine this with the fact that a large portion of the fibers coming in at the optic nerve represent a small central portion of the visual field-the fovea. With every eye movement, the neural pattern in the optic nerve changes. This means that vision is not just a problem of spatial pattern recognition, but of time-based patterns. The temporal nature of vision has been ignored by almost all theories dealing with vision. The key to understanding vision is to understand the importance of the time-varying patterns. By the way, hearing and touch work in the same way as well.
TR: Hearing seems clearly related to time-based patterns. But touch?
HAWKINS: Sure-the role of the fovea is played by your fingertips and the role of the saccade is played by the movement of your fingers over an object. Feeling an object creates a time-varying pattern. As the neocortex suggests, a common mechanism underlies vision, touch and hearing.
TR: How does this fit in with your model of the brain?
HAWKINS: You have to consider it together with the dominant nature of feedback. People tend to view the brain as a sort of input-output box. The input comes in, it gets processed, and out pops the result and you do the right thing. Well, if you look at the interconnections in the brain, there are many more fibers feeding backward than feeding forward. There’s more information traveling toward the input areas than there is toward the output areas-the ratio can be as high as 10 to 1. This is again something that is well known, but generally ignored because people don’t know what to make of it.
TR: OK-what should we make of it?
HAWKINS: One of the biggest implications is that parts of the brain look like what are called autoassociative memories. This is a type of memory that was partially inspired by neural architectures. It means that you provide part of what you’re looking for and you get the rest of it back. Clearly, that’s something brains are good at-memory is aided to a huge extent by context. You’re given a clue to something-say a taste or smell or image-and then you follow this progression of autoassociative recall.
TR: And you see this as leading to a theoretical model of how the brain functions?
HAWKINS: Yes, but there are problems. People who have studied the mathematics of autoassociative memory structures have found that if you make big autoassociative memories, they can’t store enough data. That is, if I make the memory 10 times larger, I can’t put 10 times as many data items in it. I can put bigger data items in it, but I can’t put more data items in it. So people have struggled with autoassociative memories as a model for brain function, because they have too limited a capacity.
TR: So why do you want to go back to them?
HAWKINS: Because I had a different approach. The earlier studies had been trying to apply autoassociative memory only to spatial data. But if you apply autoassociative memories to time-based data, you might be able to overcome their limitations. Remember, when you have bigger and bigger autoassociative memories, you can’t store more items-but you can store bigger items. If I view those bigger items as time-based data constructs, then I may not know a tremendous number of things, but I know a tremendous number of temporally connected things.
TR: What does all this have to do with intelligence?
HAWKINS: It goes back to my view that the brain is not just an input-output box. I think that intelligence is an ability of the organism to make successful predictions about its input. Intelligence is an internal measure of sensory prediction, not an external measure of behavior. When you look at my face, your eyes don’t just go randomly around. They look at very specific things. Typically they will look from eye to eye to nose to mouth. What your brain is doing during this process is saying, essentially: I see a pattern here that might be a face, and this might be an eye. And if I see an eye here, there should be another eye over there. It’s expecting a certain neural firing pattern at that instant. If you were to look at a face, and see a nose where an eye should be, then you’d know immediately something was amiss.