The U.S. Intelligence Advanced Research Projects Activity (IARPA) has granted more than $28 million to Harvard University's John A. Paulson School of Engineering and Applied Sciences, Center for Brain Science, and Department of Molecular and Cellular Biology to develop advanced machine-learning algorithms.
The goal is to fulfill IARPA's challenge to discover why brains are so good at learning, and use that information to design computers that can interpret, analyze, and learn information on a human level. Researchers will record activity in the brain's visual cortex, plot out its connections at a previously untried scale, and reverse-engineer the data to develop better algorithms for learning.
The project initially will train rats to visually recognize objects on a computer screen, recording their brain activity as they learn. Afterwards, a slice of the trained rat's brain will be imaged by an advanced scanning electron microscope, generating more than a petabyte of data. The data will then be processed by algorithms to reconstruct cell boundaries, synapses, and connections, and visualize them in three dimensions.
The next task will be to determine how the brain uses those links to rapidly process information and infer patterns from new stimuli, and from there algorithms for learning and pattern recognition inspired and limited by the connectomics data will be built.
From Harvard Gazette
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