Quanta Magazine: Three teams composed of neuroscientists and computer scientists will attempt to figure out how the brain performs these feats of visual identification, then make machines that do the same. By Emily Singer
'“Today’s machine learning fails where humans excel,” said Jacob Vogelstein, who heads the program at the Intelligence Advanced Research Projects Activity (IARPA). “We want to revolutionize machine learning by reverse engineering the algorithms and computations of the brain.”
'Time is short. Each team is now modeling a chunk of cortex in unprecedented detail. In conjunction, the teams are developing algorithms based in part on what they learn. By next summer, each of those algorithms will be given an example of a foreign item and then required to pick out instances of it from among thousands of images in an unlabeled database. “It is a very aggressive time-frame,” said Christof Koch, president and chief scientific officer of the Allen Institute for Brain Science in Seattle, which is working with one of the teams.
'Koch and his colleagues are now creating a complete wiring diagram of a small cube of brain — a million cubic microns, totaling one five-hundredth the volume of a poppy seed. That’s orders of magnitude larger than the most-extensive complete wiring map to date, which was published last June and took roughly six years to complete.
'By the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. That tiny portion houses about 100,000 neurons, 3 to 15 million neuronal connections, or synapses, and enough neural wiring to span the width of Manhattan, were it all untangled and laid end-to-end.'