We are studying neural/analog inspired computation. Neural/analog inspired models are interesting for several reasons. First, these models are useful because they lead toward fast, ultra low-power analog VLSI hardware. Second, we study these models in order to better understand the information processing tasks necessary for artificial and biological computational systems. Finally (and ironically), these algorithms can lead to efficient DSP-based algorithms that have a much wider applicability than if they solely existed in analog hardware.
  

We are studying biologically-inspired analog signal processing techniques in several application areas. Many real-world applications become feasible because of the high-speed, compact-size, low-cost, and low-power consumption of the analog chips under design. Special-purpose analog VLSI hardware is currently under development for 3-D sound localization and processing, analog speech synthesis, visual motion detection, wavelet image enhancement, adaptive filters, and chaotic oscillators. Ultimately this research will result in products for speech recognition preprocessors, hearing aids, teleconferencing, real-time image processing, and intelligent automobiles.
  

 We are also building analog VLSI circuit models in order to gain a better understanding of neurobiological computation. Since analog circuits operate under many of the same power and communication restrictions imposed upon their biological counterparts, it is hoped that the silicon models will provide insights into these biological information processing systems. We believe that we cannot easily model detailed biological systems with analog VLSI since the two media differ significantly at their lowest levels, however, these silicon systems can provide insights in neurophysiologic organization--especially for higher level-brain functions where models cannot realistically model every biological detail. A indirect benefit of using analog VLSI to study biological computation, is the development of devices for the handicapped such as improved hearing aids for the deaf, artificial vision systems for the blind, and collision-warning sensors for wheel-chairs.
  

Fully analog implementations are poorly matched for tasks requiring long-distance communication, long-term storage, high accuracy, flexibility, or general purpose computation. Thus, realistic systems are typically hybrid systems that utilize digital circuitry to deal with the above mentioned problems and analog hardware, at the very least, is required to interface to the mostly analog world. We believe that there is a larger role for analog systems than to serve as simple A/D interfaces. Two particularly promising areas we are investigating are front end sensor processing and dynamical systems modeling.