Unsupervised Learning

Competitive Learning

Neurons compete against each other to be activated. Only a single output neuron active at a time (winner-takes-all neuron).

Self-Organizing Feature Map (SOFP)

Each sensory input is mapped into (associated with) a corresponding area of the cerebral cortex.

Single layer of computation neurons. Only the winner neuron gets activated. The winner neuron & its neighbors are allowed to learn, where the neighbors are neurons in close physical proximity to the winner.

  • Connections
    • Forward connection
    • Lateral connection
      • Produce excitatory or inhibitory effects, depending on the distance from the winning neuron
      • Achieved by Mexican hat function

Mexican Hat Function

Algorithm

  1. Init weights
  2. Activation & finding winner
    1. $$minj |X - W_j| = min_j \sqrt{\sum{i=1}^n (xi - w{ij})^2}$$
  3. Learning by updating weights
    1. $$w'{ij} = w{ij} + \Delta w{ij} = w{ij} + \alpha (xi - w{ij}) \text{ if activated}$$
  4. Iteration
    1. Minimum-distance Euclidean distance satisfied

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