Engineers have a lot to gain from studying biology. The study of biological neural systems alone provides numerous examples of computational systems far more complex than any man-made system that perform real-time sensory and motor tasks in a manner that humbles the most advanced artificial systems. Despite the evolutionary genesis of these systems and the vast apparent differences between species, there are common design strategies employed by biological systems that span taxa, and engineers would do well to emulate these strategies. However, continuous-time parallel biologically-inspired computational architectures do not map well onto conventional discrete-time serial processors. Rather, an implementation technology that is capable of directly realizing the layered parallel structure and nonlinear elements employed by neurobiology is required for power and space efficient implementation. Custom neuromorphic hardware meets these criteria, and yields small, light, low power dedicated sensory systems that are ideal for autonomous robot applications. As examples of how this technology is applied, this article describes both a low-level neuromorphic hardware emulation of an elementary visual motion detector, and a large-scale system-level spatial motion integration system.
Charles M. Higgins, ``Sensory architectures for biologically-inspired autonomous robotics,'' The Biological Bulletin, vol. 200, pp 235-242, April 2001.