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AHaH Computing–From Metastable Switches to Attractors to Machine Learning

Figure 12

Attractor states of a two-input AHaH node under the three-pattern input.

The AHaH rule naturally forms decision boundaries that maximize the margin between data distributions. Weight space plots show the initial weight coordinate (green circle), the final weight coordinate (red circle) and the path between (blue line). Evolution of weights from a random normal initialization to attractor basins can be clearly seen for both the functional model (A) and circuit model (B).

Figure 12