Efficient precise computation with noisy components : extrapolating from an electronic cochlea to the brain

Citation

Sarpeshkar, Rahul (1997) Efficient precise computation with noisy components : extrapolating from an electronic cochlea to the brain. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/FSBH-QA93. https://resolver.caltech.edu/CaltechETD:etd-08092005-104717

Abstract

Low-power wide-dynamic-range systems are extremely hard to build. The cochlea is one of the most awesome examples of such a system: It can sense sounds over 12 orders of magnitude in intensity, with an estimated power dissipation of only a few tens of microwatts. We describe an analog electronic cochlea that processes sounds over 6 orders of magnitude in intensity, while dissipating less than 0.5mW. This 117-stage, 100Hz-10Khz cochlea has the widest dynamic range of any artificial cochlea built to date. This design, using frequency-selective gain adaptation in a low-noise traveling-wave amplifier architecture, yields insight into why the human cochlea uses a traveling-wave mechanism to sense sounds, instead of using bandpass filters. We propose that, more generally, the computation that is most efficient in its use of resources is an intimate hybrid of analog and digital computation. For maximum efficiency, the information and information-processing resources of the hybrid form of computation must be distributed over many wires, with an optimal signal-to-noise ratio per wire. These results suggest that it is likely that the brain computes in a hybrid fashion, and that an underappreciated and important reason for the efficiency of the human brain, which only consumes 12W, is the hybrid and distributed nature of its architecture.

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