Simply put, conventional flight control uses a little measurement and a lot of computation. I believe that the fly does exactly the opposite: a lot of measurement from many sensors and a little computation. I call it the sensor-rich feedback control paradigm.

The fly brain receives sensory inputs from about 80 000 sites on its body, so about 98 percent of the neurons are specialized, devoted to sensory processing. The remaining 2 percent take care of higher-level functions, such as flight control, recognizing predators, and the like. Of course, the fly has many tasks other than flying, so quite a few of its sensors aren't related to flight, such as those for taste, smell, sound, temperature, and humidity.

A human being has thousands of muscles; between your elbow and your fingertips, you have 200 degrees of freedom. A fly, by contrast, is not actuator-rich: it uses only 12 or so muscles for flying, so it can produce only a relatively small number of motions.

With each wing beat, the leading edge of its wings traces a sideways figure eight in the air [see illustration, "Poetry in Motion"]. First the wings sweep forward, generating lift. Then, at the end of the stroke, they rotate about 90 degrees and sweep backward, also generating lift. At the end of the back stroke, they rotate again and sweep forward, starting the cycle again. Despite their small complement of muscles, flies execute these intricate beats 120 to 250 times per second.

For flight, the sensors of critical importance are the compound eyes and various mechanical sensors, such as the antennae and numerous wind-sensitive hairs, which allow detailed measurements of the airflow. Unique among insects, flies also have special organs for sensing their own rotation, called halteres [see illustration, "Bug Baton"].

These drumstick-shaped protrusions on the fly's thorax are the remnants of a second pair of wings. The halteres beat just like wings, but they don't generate any lift. Instead, sensors in the sockets of the halteres detect their position, which in turn helps stabilize the insect. Without them, the fly can't fly.

Most of the fly's neural processing is devoted to vision, and its compound eyes are the key to flight control [see illustration, "The Eyes Have It"]. They not only enable the fly to see static, pixelated patterns, but also the optic flow—that is, the fly's motion relative to its surroundings. The eyes allow panoramic vision; the fly can see nearly all of the surrounding space at once, as if its worldview were projected onto a sphere. Also notable are three light-sensitive sensors arranged in a triangle on the top of the head, called ocelli. Their main role is to detect which direction is up, so that the fly can rapidly orient itself.

Each of the fly's compound eyes is composed of up to 6000 miniature hexagonal eyes, or ommatidia. Each ommatidium measures light intensities within a small solid angle of 1 to 2 degrees. This spatial resolution is much lower than that of the human eye, but the fly eye's temporal resolution—its ability to detect motion—is higher by an order of magnitude. That's why it's so hard to sneak up on a fly.

Each ommatidium operates in conjunction with its closest neighbors, in bunches of six wired together into elementary motion detectors, or EMDs. Even though each ommatidium sees only a little bit of the surroundings, its view is compared with its neighbors', and if what the neighbors see is different, the fly senses movement. In that way, the EMD estimates the local velocity vector of the optic flow.

These concepts are best understood in an example. Let's say the fly is moving straight up. The local velocity vector recorded by each ommatidium would point down. It's like riding in a helicopter that's taking off vertically: all the buildings, trees, and lights around you will appear to be streaking downward. If you were to map all the local velocity vectors onto a sphere, representing the fly's panoramic field of vision, the sphere would be covered with downward arrows. And if you were then to take the sphere and flatten it out into a Mercator projection, all of the arrows would be pointing downward.

Different relative motions produce different vector patterns. Suppose the fly is now rolling in the air, around the lengthwise axis of its body. The fly would see objects around it appearing to move in the direction opposite to its roll. When you project the local vectors of the fly's motion onto a sphere, they all head in one direction around the horizontal axis. But in the flattened projection of the vector pattern, some of the local vectors point upward, some downward, and some in between.