Pattern recognition (e.g., face, voice recognition) remains one of the most difficult problems for computer science. Odors are complex physical objects (blends of molecules) and yet, are perceived as singular objects (e.g., coffee, jasmine); because olfactory circuit designs appear similar across animal species (from insects to mammals), olfaction constitutes a potentially ideal system to identify key solutions to pattern recognition. The investigators are an interdisciplinary team from Biology, Physics, Computer Science and Electrical Engineering from Caltech and from the University of California-San Diego. They are investigating the basic computational principles of olfactory processing and recognition in animals, with the long-term goal to build "intelligent" pattern storage and recognition devices, designed using rules inspired by neurobiology.
We plan to study how to build simple and later, scale up, both in number of processors and in frequency, "electronic antennal lobes and mushroom bodies" so that one might, in the future, adapt and exploit the design of biological pattern recognizers and provide new computational paradigms for human uses.
