MIT researchers are seeking to revolutionize AI technology by reproducing a vital component of the neural circuitry of the zebra finch
by Steve Nadis
One month after being hatched, male zebra finches start learning to sing by imitating the songs of their fathers. Practicing thousands of times a day, young finches master these songs in a few months so they can eventually pass on the classics—sometimes used in courting rituals—to the next generation.
For decades, scientists have recognized that the songbird learning process could shed light on how humans acquire languages and other skills. Now, a team of MIT researchers is going a step further: in a novel collaboration supported by the MIT Quest for Intelligence, they are trying to revolutionize artificial intelligence (AI) technology by reproducing, in the form of computational hardware, a vital component of the zebra finch’s neural circuitry.
The lead investigators—Jesús del Alamo, the Donner Professor and professor of electrical engineering and computer science; Michale Fee, head of the Department of Brain and Cognitive Sciences and the Glen V. and Phyllis F. Dorflinger Professor, and new member of the Center for Brains, Minds and Machines; Ju Li PhD ’00, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and Professor of Materials Science and Engineering; and Bilge Yildiz PhD ’03, the Breene M. Kerr Professor of Nuclear Science and Engineering and of Materials Science and Engineering—bring to this project a broad range of expertise.
Fee, who is also affiliated with the McGovern Institute for Brain Research, has studied songbird learning from a basic science perspective since 1996, though he hadn’t focused on applications of his research until joining forces with the others this year. In a 2020 paper for Nature Communications, del Alamo, Li, and Yildiz reported on their artificial synapse device, which incorporated new materials and a new electrochemistry-based approach to emulate biological synapses, or connections between neurons. This approach yields low-energy consumption that is approximately one million-fold lower than conventional silicon-based technology and close to those of biological synapses...
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