Abstract:Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We have pursued multiple neuroscience-inspired AI efforts which may overcome these data inefficiencies, power inefficiencies, and lack of generalization. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve mach… Show more
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