An Analog VLSI System for Stereoscopic Vision 
Misha Mahowald
Springer
An Analog VLSI System for Stereoscopic Vision  investigates the interaction of the physical medium and the  computation in both biological and analog VLSI systems by synthesizing  a functional neuromorphic system in silicon.   In both the synthesis and analysis of the system, a point of view from  within the system is adopted rather than that of an omniscient  designer drawing a blueprint. This perspective projects the design and  the designer into a living landscape. The motivation for a  machine-centered perspective is explained in the first chapter. The  second chapter describes the evolution of the silicon retina. The  retina accurately encodes visual information over orders of magnitude  of ambient illumination, using mismatched components that are  calibrated as part of the encoding process. The visual abstraction  created by the retina is suitable for transmission through a limited  bandwidth channel. The third chapter introduces a general method for  interchip communication, the address-event representation, which is  used for transmission of retinal data. The address-event  representation takes advantage of the speed of CMOS relative to  biological neurons to preserve the information of biological action  potentials using digital circuitry in place of axons. The fourth  chapter describes a collective circuit that computes stereodisparity.  In this circuit, the processing that corrects for imperfections in the  hardware compensates for inherent ambiguity in the environment. The  fifth chapter demonstrates a primitive working stereovision system.    An Analog VLSI System for Stereoscopic Vision contributes to  both computer engineering and neuroscience at a concrete level.  Through the construction of a working analog of biological vision  subsystems, new circuits for building brain-style analog computers  have been developed. Specific neuropysiological and psychophysical  results in terms of underlying electronic mechanisms are explained.  These examples demonstrate the utility of using biological principles  for building brain-style computers and the significance of building  brain-style computers for understanding the nervous system.