MIT Department of Electrical Engineering & Computer Science

Efficient Precise Computation with Noisy Components: Extrapolating from an Electronic Cochlea to the Brain

Rahul Sarpeshkar
California Institute of Technology

Abstract

I will describe and demonstrate 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.

I will show 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 the human brain, which consumes only 12W, is tremendously efficient in its information processing because of the hybrid and distributed nature of its architecture.