The fast start is an escape response that involves the highest accelerations ever measured in fish, over ten times the acceleration due to gravity.[101] For comparison, astronauts on the US space shuttle experience maximum accelerations of about three Gs when the main engines ignite for the last minute of orbit-reaching propulsion.[102] The simultaneous firing of nearly all of the muscles on one side of the fish’s body make these incredible accelerations possible. This muscle activity is coordinated by a purpose-built neural circuit called the recticulospinal system.[103] The reticulospinal circuit activates the motor neurons of the muscle after it receives a stimulus from the eighth cranial nerve, the nerve that is connected to the inner ear and the lateral line of the fish. Sounds like predator detection if you ask me. Given that the lateral line runs all the way to the tail in many fishes, this is like having the proverbial eyes in the back of your head—or, um, body.
Here’s the really cool part: if the fish is swimming around when it detects a predator, this escape-response neural circuit overrides the swimming-around circuit! That’s subsumption, baby. This override was demonstrated in a series of elegant experiments on goldfish by Joe Fetcho, professor of neurobiology at Cornell University.[104] He and Karel Svoboda directly measured the nerves’ activity in the fast-start and steady-swimming circuits. The neural signals for steady swimming comes from a system of so-called central pattern generators, clusters of neurons that drum along at a steady rhythm without much input from other circuits. Inputs from the fast-start circuit, though, immediately switch off steady swimming when escape is activated.
FIGURE 5.8. Like a fish, Tadro4 is built to decide when to forage for food and when to escape from predators. The decision to switch behaviors is made using a two-layer subsumption architecture: Tadro4 forages for food until the escape response has been triggered, at which time the foraging is switched off until the escape is complete. Every sensor on Tadro4 can be thought of as continuously answering a question: where’s the food (eyes)? Where’s the predator (lateral line)? The specific answers provide the continually updated perceptions that alter the state of the embodied-brain and drive the immediate actions of Tadro4.
Our design of Tadro4 is propelled, if you will, by what we know about how living fish respond to predators in terms of the neural circuitry, swimming behavior, and evolution. Because we know so much on so many different levels, the predator-prey system is an excellent one for testing hypotheses about the evolutionary origins of vertebrae.
Vertebrae? Remember them? We’ve lost sight of these axial structures as we probed embodied brains and intelligent behavior. For Tadro4, we created an axial skeleton that had actual vertebrae. So we resolved to see how a population of Tadro4 prey responded, in terms of the number of vertebrae, to selection imposed by a predator. The game of life continues.
Chapter 6
PREDATOR, PREY, AND VERTEBRAE
WE ALL KNOW WHY PREDATION IS SUCH A STRONG SELECTION force: dying sucks. Those about to die, here to salute you, are the Tadro4 prey, introduced in the last chapter as the feed-or-flee Evolvabots. Even though the Tadro4s bear the name Tadro, they are, compared to Tadro3, a different kettle of fish. The most important changes have to do with what Tadro4 and its whole new world seek to modeclass="underline" vertebrates in a prey-versus-predator world. Tadro4 has to be equipped with a new nervous system and a new body in order to eat and try not to be eaten. And, to pull this all off, we also needed to design and build a predator capable of tracking and chasing the Tadro4 prey.
When you are in the middle of a big, multiyear project like the Tadro3-athon, you have plenty of time to second-guess your no-turning-back decisions. We’d regularly convene our whole team of Vassar and Lafayette College researchers, give progress reports, teach one another about our different fields, and then try to find gentle ways to tell our colleagues and students that their work was a piece of sh …, sh …, shaving cream. That’s the charm and the curse of the academic: you are trained to criticize anything that moves. We are trained to be intellectual predators. As professorial types, we model this predatory behavior for our students, stalking not people but the ideas that they use and generate. We also model being the prey, the one presenting an argument to the circling sharks.
All students are prey. Sorry. When we are in student mode, we have to admit first to ourselves and then to others that we don’t know something. Talk about vulnerability! Who wants to be an openly vulnerable prey item? But ignorance or misunderstanding is a problem: no way around it. So anytime we want to get better at what we are doing, we have to admit that we have problems. As Jenny Ming, cofounder and past president of the clothing retailer Old Navy, said, “You can’t fix something if you don’t admit it’s wrong.”[105]
Okay. What’s wrong with Tadro3? I’ve already taken you through our acute crisis of confidence in Chapter 4. When the population of Tadro3s wasn’t evolving as we predicted, we switched our collective behavioral mode from conduct-experiments-and-analyze-data to stop-and-look-for-mistakes. We found the mistake that turned out to be a happy one: the wobble of Tadro3 was not a sign of energy inefficiency but rather a metric of enhanced maneuverability. That problem was tactical. On the strategic level chronic problems can’t be solved in the same way: they are a by-product of making plans. Once a particular set of strategic plans are set in motion, chronic problems persist unless you revise and start over or until you evaluate the strategically flawed experiment and design the next one.
On the strategic level, you can argue, as my colleague in biology did way back in Chapter 1, that modeling itself is a conceptually flawed process. You won’t be surprised, from everything I’ve said about modeling, that I disagree with the no-modeling critique.
At the same time, I agree with Barbara Webb (see Chapter 3): we need to justify carefully the specific modeling approach that we take. So now that you’ve been through our design process (Chapter 3), seen the Tadro3 model system in action (Chapter 4), and gotten a taste of the mechanisms that help us understand Tadro3’s behavior (Chapter 5), we can revisit Webb’s criteria for judging the value of our biorobotic model.
By Webb’s criteria, have we produced a good model? Recall that in addition to “relevance” (= able to test a hypothesis) and “medium” (= physical basis of robot), we selected “behavioral match” and “mechanistic accuracy” as two other figures of merit for our Evolvabot models. Do we see matched behaviors and accurate mechanisms within the hierarchy of skeleton 
individuals population? In terms of behavioral match, the complexity of the evolutionary patterns in the Tadro3 system is evidence that the system behaves realistically on the population level. Further, because we know that evolutionary mechanisms of selection and randomness cause those complex patterns, the accuracy of those mechanisms is also high.
But what about the mechanistic accuracy of the Tadro3 itself, the levels in our hierarchy of individual and skeleton? When we present our evolutionary biorobotic work at the Annual Meeting of the Society for Integrative and Comparative Biology, I let our students know that they can expect a visit from a constructive predator from the laboratory of Robert Full, professor and director of the Poly-pedal Laboratory at the University of California. Full is world famous for his careful experimental study of invertebrates, isolation of functional principles that unify seemingly different behaviors, and implementation of those principles in the design and operation of biomimetic robots.[106] So when Full or one of his colleagues arrives to critique, we are, as the Ferengi say, all ears.[107]
101
For a review of fast starts in the context of predatory-prey situations, read: P. Domenici, “Scaling of Locomotor Performance in Predatory-Prey Encounters: From Fish to Killer Whales,”
102
This figure of 3 G during takeoff is from mission specialist Koichi Wakata and can be found at the NASA website: \spaceflight.nasa.gov/feedback/expert/answer/crew/sts-92/index.html.
103
For a lovely review of this fast-start escape circuit, see the following papers: S. J. Zottoli and D. S. Faber, “The Mauthner Celclass="underline" What Has It Taught Us?”
104
My favorite paper of Fetcho’s on the control of swimming behaviors is K. R. Svoboda and J. R. Fetcho, “Interactions Between the Neural Networks for Escape and Swimming in Goldfish,”
105
From an interview by
106
I encourage you to explore Full’s excellent website: http://polypedal.berkeley.edu/cgi-bin/twiki/view/PolyPEDAL/WebHome.
107
A reference to Ferengi first officer Kazako from “The Battle,” an episode of