What behavioral neuroscience shows beautifully is just what Trimmer had said: every brain has a body. Once more, with feeling: understanding behavior involves not just the neural circuit but also the neural circuit placed within the nervous system, the nervous system connected to sensors and muscles, the sensors and muscles part of a particular body, and the particular body interacting with the physical world, including other agents.
We haven’t, I realize, built a brain from the ground up. But by starting with circuits, we are making progress in terms of understanding how brains operate. By combining the brain basics of behavioral neuroscience with the functionalism of artificial intelligence, we come to three inescapable conclusions:
* Every brain has a body, both in terms of cooperational physiology and connective anatomy. The brain alone is not sufficient to explain behavior.
* The embodied-brain has some functions that it shares with computers and microcontrollers and some that it does not.
* Some kinds of functions that we associate with the structure called the vertebrate brain we can see in so-called simple[89] organic and artificial agents that have no brain; thus, the brain doesn’t control or determine all behaviors. The brain is not necessary for behavior.
This last assertion is probably the most controversial. Begging to differ might be George Lakoff, who helped develop the concept of the embodied mind within the fields of philosophy and cognitive linguistics.[90] Lakoff, writing about his development of the Neural Theory of Language, states, “Every action our body performs is controlled by our brains, and every input from the external world is made sense of by our brains. We think with our brains. There is no other choice.”[91] As you can see, Lakoff’s embodied perspective is still a neurocentric one (see Figure 5.2). So let’s get rid of the brain altogether and see what happens!
If, as Trimmer says, the body interacting with the world is doing part of the computational work of the nervous system, then we ought to be able to see bodies with very little brain or even no brain doing interesting things as autonomous agents.[92] Sound familiar? Doing just that is Tadro3, as I claimed at the beginning of this chapter. So let’s continue to use Tadro3 to see how far we can push the idea of being intelligent without having a brain. I’ll try to convince you that Tadro3 approaches the limit of being the simplest autonomous agent possible. By pushing the limits, I hope to show you that all it takes is a little KISS to create intelligent behavior.
You’ve seen Tadro3’s neural programming (see Figure 5.1). Let me translate the central computation from its computer code into mathematical terms so you can see how simple its programming is. The computer code takes a voltage input from Tadro3’s single eyespot and converts it to an intensity value, i, that is, in turn, converted to a value, ß (Greek letter beta), which represents a turning signal for the tail, the tail angle:
ß(t) = i(t) x c
where the t indicates that both i and ß are changing through time, t, and c is a “constant of proportionality,” a numeric fudge factor that scales the light intensity to the size that we need to calculate a realistic tail angle. In words, this equation can be read as follows: “The angle of the tail at any time is linearly proportional to the intensity of light hitting the photoresistor at any time.” That’s it. It’s hard to imagine a much simpler equation with variables. (I know, if you got rid of the c then it’d be even simpler, but at that point you’d have a simple identity equation.)
As brains go, this doesn’t qualify. If we built a circuit that would perform this computation, you’d likely see something like this in a vertebrate (Figure 5.3): a sensory cell with a membrane potential that varies continuously with light intensity, a primary sensory neuron that converts the sensory cell’s graded input into a train of action potentials and connects to two other neurons, one an inhibitory interneuron that reduces the activity of the motor neuron connected to the left-turning muscle and the other a motor neuron, without an intervening interneuron, connected to the right-turning muscle. This simple circuit has only four neurons and three other cells that complete the sensory-motor system.
Let’s make the circuit even simpler! We can think about how a bioengineer might try to accomplish the task using wetware, cells and proteins of biological origin that she can arrange as needed. In her build-a-brain workshop she could make the circuit simpler by creating a receptor that connects directly to the muscle cells without any neurons at all (Figure 5.3). Let’s presume that this simple circuit is, in principle, possible. Then this bioengineering design raises an awkward question: why have vertebrates made such a muddle of their circuit design? Why don’t they go all the way with the KISS principle?
To put it another way: why go through the trouble of building a chain of multiple cells? There are good reasons. What you get with more neurons is more synapses. Each synapse, because it converts electrical signals to chemical ones, is a place where you can regulate and adjust how a neuron or muscle is responding to the “upstream” cell signaling the “downstream” cell. These cell-level adjustments are important in creating functional changes of the circuit during development and learning. Another consequence of having multiple neurons is that you can increase the number of connections that the circuit makes with other circuits (branching connections not shown), increasing opportunities for coordination and computation.[93]
FIGURE 5.3. Designing the nervous system of Tadro3 in wetware. The top circuit, built in the way that vertebrates build neural circuits, contains seven cells: one receptor, one sensory neuron, one inhibitor interneuron, two motor neurons, and two muscle cells. The bottom circuit, built in a way that a bioengineer might be tempted to try, contains only three cells: one that directly innervates the two muscle cells. Both hypothetical neural circuits have the same function: in the presense of light on the receptor, decrease the activity of the left-turning muscle and increase the activity of the right-turning muscle. The gaps between the cells represent synapses, across which cells communicate by diffusing chemical neurotransmitters. A synapse is excitatory if unlabeled or labeled with a positive sign. A synapse is inhibitory if labeled with a negative sign. The large circles with smaller embedded dark circles represent the cell bodies of the neurons.
FIGURE 5.4. Tadro3 morphed into a wheeled vehicle. The single light sensor (cup) sends a reverse (–sign) and a forward (+ sign) signal to the two motors (small black rectangles) that independently control the two wheels (large black rectangles) that spin at different rates.
If you look at the neural circuits in Figure 5.3, you’ll notice that we’ve done it again: we forgot the body! To be fair, we did this on purpose so that we could see what an isolated Tadro3 nervous system might look like. Note, also, that this circuit is not a brain in the anatomical sense of Brusca and Brusca’s invertebrates: it is not a mass of neurons. This is a diffuse nervous system, and it needs a body. We can create a body that is as similarly abstract as the bioengineer’s neural circuit. To keep it simple, let’s put the Tadro3 nervous system into a wheeled vehicle (Figure 5.4). With a body operating on land, by the way, we don’t have to worry about all the crazy physics of swimming that we mentioned previously.
89
I really hate the use of the word “simple” in a context like this because it comes packed with so much anthropocentric baggage. One item in our luggage is that we humans assume that we are the most “complex” organisms by any measure. But consider a single-celled organism: in one cell it packs all the basic functions—like eating, moving, and reproducing—that we humans need a multicellular body to perform. As we go on, you’ll see that I call Tadro3 “simple” with specific reference to its sensory-motor system. That’s okay, I’d argue, because I’m being explicit about the system of comparison. Implicit “simplicity” means a thousand different, unspoken things.
91
George Lakoff, “The Neural Theory of Metaphor,” in Gibbs,
92
Louise Barrett,
93
Neurocomputational modeling of swimming vertebrates by Örjan Ekeberg and then Auke Ijspeert have shown that many, many possible circuit structures will produce the same function (functionalism rules!). Thus, we shouldn’t take the two T3 circuits here as the only ones that are possible. Örjan Ekeberg, “A Combined Neuronal and Mechanical Model of Fish Swimming,”