Herself’s Artificial Intelligence

Hebbian Learning

“When the axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” [D.O.Hebb, The Organization of Behavior]

In other words, in a neural net, the connections between neurodes get larger weights if they are repeatedly used during training.

There are adjustments that have been made to this rule. Weights are bounded between -1.0 and 1.0. Neurodes that are not used are decreased in value. Neohebbian Learning takes this into consideration. It iteratively computes each nodes connection weights using NewWeight = OldWeight F*ForgottenWeight + N*NewLearningWeight. F, N are constants between 0 and 1.0, F being how quickly to forget and N being how quickly to learn.

Differential Hebbian Learning adjusts the learning and forgetting by pro portion to the amount of change in weight since last cycle. Which is just the derivative of the neurode’s output over time.

Drive reinforcement theory developed by Harry Klopf is a learning system that modifies differential Hebbian learning. The weight increase depends on the product of the change in the output signal of the receiving neurode and the weighted sum of the inputs over time. This allows some temporal learning to occur in the system. This system is closer to the classical conditioned training done by Pavlov. 207

See also:
Reinforcement Learning