A new paradigm for artificial intelligence
Since the 1950’s the term “artificial intelligence” has evoked the use of computers to emulate the functioning of the brain, but in practice was implemented by circuits and systems which did not mirror brain circuitry. Even the “neural networks” intensively explored since the 1980s are built with simplified “neurons” consisting of point-like units with no spatial structure.
In contrast, real neurons in the brains of animals are electrically active tree structures, with thousands of inputs scattered over tens or hundreds of quasi-independent branches. Not only are inputs integrated nonlinearly in these branches, but patterns of input elicit a host of local interactions which adaptively control the excitability of the branches and the properties of the inputs themselves. Biological neural circuits can therefore be described as an electrically active and self-regulating computation fabric, which embeds a host of interwoven mechanisms to confer enormous computational power.
This talk will describe how systems are being built and embedded with some of the many local neural mechanisms discovered by neuroscientists, and already exhibit certain computational powers beyond prior artificial sensory systems.
Dr. Paul Rhodes has been a Visiting Scholar at Stanford University and is the head of Evolved Machines, Inc., which is pioneering the synthesis of artificial neural circuits and their application to olfaction and visual object recognition.
Evolved Machines aims to develop the first generation of devices truly based on brain circuitry, pioneering the fusion of neuroscience and engineering to develop new categories of machines which embed some of the capacities of biological neural systems.
Dr. Rhodes’ academic research in the 1990s concerned simulations of cortical neurons and circuits. He received a Ph.D. and M.S. in neuroscience at NYU Medical School, and received an M.S. in Physics from Stanford after graduating with an A.B. in Physics, Magna cum Laude, from Harvard.
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