Canadian psychologist Donald O. Hebb (1904-85) obtained his Ph.D. from Harvard University in 1936, at age 32, and was interested in how people learn and remember. Earlier researchers such as Cajal had suggested that synapses may underlie the brain mechanisms of learning and memory, and Hebb wondered what kind of synaptic activity might be involved. In 1949, Hebb published a book, The Organization of Behavior, in which he sketched some of his theories.
Hebb was struck by how the mind associates different objects to form a pattern or a memory. For example, the sight of a trophy might remind a person of the event or game in which the trophy was won, and the memory of this event, in turn, may spark other memories, such as the congratulations of parents and friends, and so on. Memories often form a chain of associations, with one memory invoking the next. Perception functions in a similar way, with a stimulus, say an apple, invoking a chain reaction, perhaps leading to the realization that one is hungry. But no one knows exactly how such associations form in the brain.
One of Hebb's most important hypotheses is that the connections between neurons increase if their activity is correlated. Suppose that neuron A makes a synapse on neuron B, though the connection is weak. (In other words, the weight of this synapse is nearly zero.) Note that A's synaptic input to B is just one of many inputs to B. If neurons A and B are simultaneously active over some period of time, Hebb hypothesized that the strength of the connection between these neurons will grow. This increase in strength means that in the future, the firing of neuron A will be more important in B's decision to fire or not, so A and B will be more strongly correlated. The association has been learned.
The strengthening of connections caused by correlated activity throughout the network will result in a chain of associations by which one item, be it a perception or memory, invokes others. This hypothesis, called Hebbian learning, has influenced computer scientists exploring artificial neural networks, as well as scientists who study the brain. Although no one knows how much of a role Hebbian learning plays in brain mechanisms, certain synapses do sometimes follow this principle.
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Hebb was struck by how the mind associates different objects to form a pattern or a memory. For example, the sight of a trophy might remind a person of the event or game in which the trophy was won, and the memory of this event, in turn, may spark other memories, such as the congratulations of parents and friends, and so on. Memories often form a chain of associations, with one memory invoking the next. Perception functions in a similar way, with a stimulus, say an apple, invoking a chain reaction, perhaps leading to the realization that one is hungry. But no one knows exactly how such associations form in the brain.
One of Hebb's most important hypotheses is that the connections between neurons increase if their activity is correlated. Suppose that neuron A makes a synapse on neuron B, though the connection is weak. (In other words, the weight of this synapse is nearly zero.) Note that A's synaptic input to B is just one of many inputs to B. If neurons A and B are simultaneously active over some period of time, Hebb hypothesized that the strength of the connection between these neurons will grow. This increase in strength means that in the future, the firing of neuron A will be more important in B's decision to fire or not, so A and B will be more strongly correlated. The association has been learned.
The strengthening of connections caused by correlated activity throughout the network will result in a chain of associations by which one item, be it a perception or memory, invokes others. This hypothesis, called Hebbian learning, has influenced computer scientists exploring artificial neural networks, as well as scientists who study the brain. Although no one knows how much of a role Hebbian learning plays in brain mechanisms, certain synapses do sometimes follow this principle.
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