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The Interactive Activation and Competition Mechanism

• the activations of each of the units can be thought of as the "degree of belief" in that hypothesis or the existence of that feature
• the weights are then an indication of how strongly belief in one hypothesis or feature implies belief in another hypothesis or feature
• if most of the active units in the network (i.e. the hypotheses that the net believes are true) are connected to this unit with positive weights, then we should increase its activation
• if most of the active units are connected to this unit with negative weights then its activation should be decreased
• the net input function does this by multiplying each of the incoming weights by the level of activation of its unit and adding these values
• so the net input for unit i is:
  net_i = w_ija_j
  where w_ij is the weight from unit j to unit i and a_j is the activation of unit j.