Hebb's rule

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Hebb's Rule or Hebb's postulate attempts to explain "associative learning", in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells.

Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability.… When an 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.[1]

Model

Given a set of k-dimensional inputs represented as a column vector <math>\vec{x} = [x_1, x_2, \cdots, x_k]^T</math>, and a linear neuron with synaptic weights from the inputs <math>\vec{w} = [w_1, w_2, \cdots, w_k]^T</math> the output the neuron is defined as follows:

<math>y = \vec{w}^T \vec{x} = \sum_{i=1}^k w_i x_i</math>

References

  1. Template:Cite book