In their new book, Big Brain: The Origins and Future of Human Intelligence, authors Gary Lynch, PhD, and Richard Granger, PhD, ask, “Does size matter?” Here are some clues:

1: Based on physical size alone, men’s brains are bigger than women’s. (But if women are right, men think with another part of their anatomy anyway.)
2: There were pre-human primates, now extinct, whose brains were relatively larger than ours.
3: Chimpanzees perform better on certain memory tasks than college students.

To knock some sense into their own skulls, Chet Cooper, editor-in-chief, and E. Thomas Chappell, MD, managing medical editor of ABILITY Magazine and neurosurgeon, recently spoke with Granger, who is a professor at Dartmouth College in Massachusetts, about the book he co-authored with Lynch, a professor at the University of California, Irvine. The two scientific researchers undertook a groundbreaking study of the human brain’s evolution. Both prominent neuroscientists, they trace human intelligence back at least tens of thousands of years to before the Boskops, pre-humans with brains 30 percent larger than ours. Granger and Lynch endeavor to understand Alzheimer’s via a computer model, to comment on the limits of the human brain, and to examine such stellar thinkers as Albert Einstein.

Chet Cooper: Gary is in California and you’re in Massachusetts… how did the two of you get together to write this book?

Richard Granger: We both used to be at UCI. I came to Dartmouth to take an interdisciplinary position. Dartmouth College founded the Neukom Institute, which is aimed precisely at the questions that interest me most. Namely, how can we understand brain cell connections as circuits? How can we understand the complex systems of the brain so thoroughly that we can actually build “simulacra” or computer models of them? The institute is in the building stages now at Dartmouth, and it’s an exciting and challenging prospect.

Cooper: When you think about building a computer model of the brain, given the complexities of what goes on with memory and the organ’s other functions, can you say how you’re going to pull that off?

Granger: Sure. There are a few key ideas. One is to consider the connections between brain cells and treat them like electrical circuits. We can actually build brain-like circuits and study them. Another notion to consider is that much of what we become as humans is learned. Some things are hard-wired, so to speak, based on evolution and genetics or DNA. Other things are acquired from our various life experiences. That combination is the one-two punch scientists are striving to understand.

We are trying to build circuits with an architecture similar to that of the brain. Computers have what is called an architecture, which is a set of circuit designs that can do such tasks as calculations. Though computers are now quite sophisticated, they can’t “think,” “recognize,” or execute many of the tasks that humans can do with their brains. How well we understand the brain is another matter entirely, but our knowledge base continues to evolve.

We can already “teach” the circuits we’ve built. We literally hold up a cup in front of a robot and say, “Here’s the cup.” Then we move it and say, “Now the cup is over here,” and then we ask it, “where did the cup go?” We teach it the relationships between objects in the real world, what it perceives, what it “hears.” The computer can respond to simple language, such as you might use with a child, to format its own internal software models for what it’s seeing and hearing. This sounds a bit like magic, but existing computer technology allows machines to change their output based on certain input. This is analogous, in a rudimentary way, to a child never touching a hot stove again.

Tom Chappell: How does this relate to Artificial Intelligence (AI)?

Granger: Attempts at AI have had a goal related to ours for a long time. Much of the early study in this area was not focused on the brain. It was focused on studying human behavior, and trying to see if we could imitate that behavior with a computer. In all fairness, this is a bit like trying to understand a car without looking under the hood. More recent endeavors pay attention to what the actual mechanisms or the “engines” of the brain are and how they actually work. Our idea is to better understand the intricacies of brain function to give us a realistic shot at building robotic brains.

Cooper: I guess one of my concerns is the concept of ignorance and how we might replace a person’s ignorance with a certain idea. That’s the good thing about ignorance—it’s curable. If you introduce the person to information in a specific way, you may get them to understand it. Often it seems the only way for a person to understand something is by experiencing it. I’m talking about temperament theory, specifically a temperament that is rigid. The only way a person with a rigid temperament is going to change their thinking is by having an experience that is different from what they perceive to be the truth.

So, I’m wondering how that’s ever going to be possible. If you look at extremists, for instance, they truly believe what they believe. There seems to be no way to change their views, no matter how inaccurate they are. So the question is, if you could understand the brain mechanisms that support this kind of thinking, could you change it for the better? And, if you could, would it be ethical to do so?

Granger: That’s a tough issue, and one that’s on all of our minds these days. Picture a teenager who just doesn’t realize that a car really is dangerous until he gets in his first accident. At that point, his whole conception of what he’s doing while he’s driving changes. The ability to simulate experiences is already happening in what we now call virtual reality. Many kids these days are exposed to this in video games with uncertain results, to be sure. If we can train people by simulating reality so that they perceive realistic experiences, it may be a superior way to learn. If, however, you are talking about a person unwilling to learn new ideas, that’s a different story entirely. One certainly doesn’t want to turn to thoughts of “brainwashing.”

Chappell: I’m curious about the scientific approach you use. Are you studying the “circuitry” of the brain and then trying to emulate it with electrical circuits, or are you learning more about electrical circuits and then trying to see if they apply to the brain, or both?

Granger: It’s the former, much more than the latter. We’re trying to take the computational and engineering knowledge that we have and apply it to brain function, to help us see if we can understand what kind of “machine” a brain really is.
However, working in the other direction is quite interesting, as well. If we can understand brain circuits, we can build artificial circuits that might be able to “plug” into brains. Circuits that might be able to act as prosthetics and improve or repair brain function.

Chappell: The “quantum leap” is the connection between the brain and the mind. Eric Kandel’s work on this problem won the Nobel Prize. As you know, he was able to show chemical changes in the rudimentary nervous system of a simple invertebrate that occurred with changes in the organism’s behavior as it “learned”—or better stated, adapted to changes in its environment. Are there circuits that are capable of making changes based on input?

Granger: Absolutely. Understanding the mechanisms for learning is a crucial piece of our work. Building on work by Kandel and many others, we’ve increased our understanding of the biophysical (electrical and chemical) changes that occur at the synapses. Synapses are the connections between neurons (nerve cells) in the brain. Understanding how biophysical changes occur in the brain during learning has led to an increasingly sophisticated understanding of how we learn. This has already led to the development of novel drugs, many of which are currently in human testing; and novel hypotheses and treatments for learning-related conditions. Such knowledge also underlies our ability to build artificial brain circuits. These artificial brain circuits, called simulacra, can also “learn” on a synapse-by-synapse basis.

Chappell: So you start out with simplistic circuits and rudimentary functions, emulating a lower organism’s nervous system and then you build on that?

Granger: Yes. A lot of that is going on in our field. Obviously, it’s easier to build simpler mechanisms, but it’s also potentially frustrating, because no one wants to build a simple mechanism and then discover that it is not on the pathway to bigger and better mechanisms. One of the reasons we’re studying the evolutionary progression of small brains to big brains is precisely because we want to know where the highway went that led to humans as opposed to some other version of our species.

Chapell: When you say “small brain” and “big brain,” you’re talking about evolution—how nervous systems have evolved from simple “lower organism” nervous systems to the mammalian brain?

Granger: That’s right. Actually, all mammals’ brains are extraordinarily similar. The brain of a mouse and the brain of a human are, except for their size, far more similar than they are different. The neurons, the chemistries and the brain areas are basically the same. The organization and the communication between those brain areas are basically the same. The differences between the brains of lower mammals and humans are enhancements of the same basic brain structure. So we do study simpler brains to better understand “big” brains. We are getting vital hints from the simple brains and studying ... continued in ABILITY Magazine

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