of both (Sarpeshkar, 2010). Engineering systems can take inspiration from biology to also compute in this fashion, and they can improve energy efficiency by delaying digitization after an optimal amount of analog preprocessing (Sarpeshkar, 2010).
One example of a bio-inspired collective analog system is the radio frequency (RF) cochlea (Mandal et al., 2009), an electronic chip that takes inspiration from the spectrum analysis of the inner ear or cochlea to create an energy-efficient and ultrafast broadband RF spectrum analyzer. This chip exploits the fact that the ear’s spectrum-analysis architecture is the fastest and most hardware efficient known to man—faster than a digital fast Fourier transform or an analog filter bank. It efficiently maps the partial differential equations that describe fluid membrane-hair cell interaction in the biological cochlea at kilohertz audio frequencies to inductor-capacitor-amplifier interaction in the RF cochlea at gigahertz frequencies. The resulting broadband RF cochlea chip operates with 20-fold lower hardware cost than a traditional analog filter bank or with 100-fold lower power than a system that directly digitizes its RF input to perform spectrum analysis. The RF cochlea is useful as a front end in advanced cognitive or software radios of the future (Sarpeshkar, 2010).
The use of analog circuits to perform energy-efficient spectrum analysis is also useful in bionic ear or cochlear implant processors for people who are profoundly deaf. Cochlear-implant processors compress spectral information present in a microphone signal in a nonlinear fashion such that it is suitable for charge-balanced tonotopic current stimulation of a cochlear electrode array implanted near the auditory nerve. For example, a digitally programmable analog cochlear-implant processor described in the literature (Sarpeshkar et al., 2005) lowered power consumption by 20-fold over a conventional design that performs analog-to-digital conversion followed by digital signal processing; it enabled flexible 86-parameter programming in a patient who understood speech with it on her first try (Sarpeshkar, 2006); it was highly robust to several sources of noise including transistor mismatch, 1/f or pink noise, power-supply noise, RF crosstalk, thermal noise, and temperature variations; and it is at or near the energy-efficient optimum even at the end of Moore’s law. Thus, this processor is amenable to fully implanted and low-cost systems of the future: Its 251-μW power consumption enables it to function on a small 100-mAh battery with 1,000 wireless recharges for 30 years. A more advanced 357-μW bio-inspired asynchronous interleaved sampling cochlear-implant processor uses a novel bio-inspired method of nerve stimulation similar to that present in the auditory nerve. It enables fine-time encoding of phase information in a signal without requiring a high sampling rate (Sit and Sarpeshkar, 2008). Hence, it enables music information to be encoded in an energy-efficient fashion without requiring a high number of electrodes or requiring high-stimulation power consumption, a bottleneck in the field of cochlear implants. It is also important for improving speech understanding in noise. Similarly, a companding algorithm inspired by tone-to-tone suppression