Tissue


'Casey Reas Casey Reas

Tissue exposes the movements of autonomous software machines. Each line in the image reveals the path of each machine's movement as it responds to stimuli in its environment. People interact with the software by positioning the stimuli on the screen. Through exploring different positions of the stimuli, an understanding of the total system emerges from the subtle relations between the simple input and the resulting fluid visual output.

The concept for Tissue originated with the book Vehicles, Experiments in Synthetic Psychology by Valentino Braitenberg, a neuroanatomist. As Braitenberg spent his career counting fibers in the visual ganglia of flies and synapses in the cerebral cortex of mice, he began to distill generalizations about how nervous systems work. He states, “I have been dealing for many years with certain structures within animal brains that seemed to be interpretable as pieces of computing machinery because of their simplicity and/or regularity [Braitenberg p.1].“ He uses the term “synthetic psychology” to define the construction of personality/behavior through building up structures of “computing machinery.” In Vehicles, Braitenberg defines a series of 13 conceptual constructions by gradually building more complex behavior with the addition of more machinery.

The software machines in Tissue are analogous to Braitenberg’s Vehicle 4. Simple layers of code combine to create the deceptively complicated behavior of these machines. Each machine has two software sensors to detect stimuli in the environment and two software actuators to propel itself. The relationships between the sensors and actuators determine the specific behavior for each machine. In the thousands of software machines simultaneously running in Tissue, there are four distinct types, each specified with a color. The architectures of the Tissue machines are shown in Figure 1. Each machine has a variable speed, direction, and position of movement and all machines share the same size, turning rate, and maximum speed. They are constrained to their environment and when they hit the edge, they reverse direction.

Each machine continually alters its direction and speed by analyzing its position in relation to the environment. First, it determines the distance from each of its sensors to the first stimulus point:

dx = stimulusX – leftSensorX;
dy = stimulusY – leftSensorY;
leftDistance = sqrt(dxdx + dydy);
dx = stimulusX – rightSensorX;
dy = stimulusY – rightSensorY;
rightDistance = sqrt(dxdx + dydy);

normSensorLeft = leftDistance/maxDistance;
normSensorRight = rightDistance/maxDistance;
normSensorLeft = hump(normSensorLeft);
normSensorRight = hump(normSensorRight);
normSensorAverage = (normSensorLeft + normSensorRight) / 2.0;

speed = speed + (maxSpeed – (maxSpeed * normSensorAverage));

direction = direction + leftDistance/turnSpeed * (1-normSensorLeft);
direction = direction – rightDistance/turnSpeed * (1-normSensorRight);