Evidence suggests that the basic structure of these modules is roughly the same throughout the cortex. Each module consists of approximately 100,000 neurons arranged in a complex network of interconnected cells.
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The sheet, roughly three millimeters thick, is made up of a series of repeating modules, or microcircuits, similar to the array of logic gates in a computer chip. It is in many ways the brain’s microprocessor. The convoluted folds covering the brain’s surface form the cerebral cortex, a pizza-sized sheet of tissue that’s scrunched to fit into our skulls. Without knowing all the component parts, he said, “maybe we’re missing the beauty of the structure.” The Brain’s Processing Units He hopes that the unprecedented scale of the Microns project will help sharpen that view, exposing more sophisticated rules that govern our neural circuits. Andreas Tolias, a neuroscientist at Baylor College of Medicine who is co-leading Koch’s team, likens our current knowledge of the cortex to a blurry photograph. While the implicit goal of the Microns project is technological - IARPA funds research that could eventually lead to data-analysis tools for the intelligence community, among other things - new and profound insights into the brain will have to come first. They found that neurons with similar functions are more likely to both connect to and make larger connections with each other than they are with other neuron types.
By pairing this map with information about each neuron’s job in the brain - some respond to a visual input of vertical bars, for example - they derived a simple rule for how neurons in this part of the cortex are anatomically connected. In a paper published in the journal Nature in March, Wei-Chung Allen Lee - a neuroscientist at Harvard University who is working with Koch’s team - and his collaborators mapped out a wiring diagram of 50 neurons and more than 1,000 of their partners. But smaller-scale efforts have shown that these maps can provide insight into the inner workings of the cortex. No one has yet attempted to reconstruct a piece of brain at this scale. 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. 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.īy the end of the five-year IARPA project, dubbed Machine Intelligence from Cortical Networks (Microns), researchers aim to map a cubic millimeter of cortex. 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. “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. 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. In conjunction, the teams are developing algorithms based in part on what they learn. Each team is now modeling a chunk of cortex in unprecedented detail. “We want to revolutionize machine learning by reverse engineering the algorithms and computations of the brain.” “Today’s machine learning fails where humans excel,” said Jacob Vogelstein, who heads the program at the Intelligence Advanced Research Projects Activity (IARPA). 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. “We are still more flexible in thinking and can anticipate, imagine and create future events.”Īn ambitious new program, funded by the federal government’s intelligence arm, aims to bring artificial intelligence more in line with our own mental powers. “Humans are much, much better generalists,” said Tai Sing Lee, a computer scientist and neuroscientist at Carnegie Mellon University in Pittsburgh. We’re also better at finding relevant information in a flood of data at solving unstructured problems and at learning without supervision, as a baby learns about gravity when she plays with blocks.
Visual identification is one of many arenas where humans beat computers. Computers, on the other hand, typically need to sort through a whole database of giraffes, shown in many settings and from different perspectives, to learn to accurately recognize the animal.
We can effortlessly grasp the most important features of an object from just a few examples and apply those features to the unfamiliar.
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