When people have trouble finding their way through office settings, there are costs in terms of poor communication, lost efficiency, time, and stress (Brill, et. al., 1984; O'Neill, 1991; Weisman, 1981; Zimring, 1981). To cope with wayfinding problems, facilities managers often have to resort to partial solutions, like complex signage, color coding schemes, and other methods to guide people. AutoNet is an experimental computer-aided design and planning tool that predicts the paths people will take through a building based on the layout of the space and their level of experience. AutoNet represents environmental information by using an artificial ‘neural network’ simulation. The mechanisms of this simulation are based on the physiology of the brain. Knowledge about the layout of the environment is represented through a network of interconnected processing elements, modeled on the behavior of groups of neurons in the brain. Thus it can create its own rules for predicting worker behavior rather than using predetermined sets of rules that a typical expert system would rely on. This system has great flexibility since there are no rules to rewrite for each setting it evaluates. The predictive validity of this simulation was empirically validated (O'Neill, 1991). This software runs within a popular and commonly available CAD software package in an MS-DOS environment. AutoNet is viewed as a “macro-ergonomic” tool to enhance the office work environment (Hedge & Ellis, 1990).
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
