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Since at least the mid-1970s, the dominant model of the brain has been hierarchical, modeled on digital computers. In brief, the hypothesis held that single neurons operated by detecting features in the environment. At the base level, neurons in the optic region of the brain, for example, would detect simple features such as lines and edges. Higher-order neurons, linked to hundreds of lower-order neurons, detected more sophisticated features: a neuron might be sensitive to red balls or green squares. At the highest level, cardinal neurons would integrate sensations to recognize concepts, such as "grandmother's face." In the hypothesis, a single neuron would be sensitive to each concept; there would thus be a "grandmother's face neuron." Memories would be laid down by changes in the ways neurons were linked to each other.
The similarity to computers is clear, with neurons replacing transistors. The brain, in this view, resembles a hierarchical corporation, with low-level office workers passing information up the chain to executives at the top and getting their orders in turn passed down the chain, each dealing only with a few superiors and subordinates.

There were problems with the hypothesis from the start. First was the problem of "binding": How could information obtained by different means be bound together into a single perception? How could the separate neurons that recognized Grandmother's face, her voice, and the word Grandmother all somehow work together to create a single perception of Grandmother? Closely linked with this was the problem of how individual neurons could produce a single consciousness. In addition, the amount of information that could be stored in even the hundreds of billions of neurons in the human brain seemed grossly inadequate to account for actual human memories, which still dwarf the memory capacities of any computer.

More critically, the hypothesis lacked experimental support. Individual neurons did respond to certain features, like lines or edges; however, they responded not to just one, but to a statistical mix of several such features. Nor could a neuron, with an average of 10,000 contacts, or synapses, from other neurons, respond reliably to the input of another individual neuron. There was no one-to-one correspondence between the response of individual neurons and any kind of information.

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