Figure 6. Steering of computation by external inputs.

The network remains sensitive to changes in its input throughout the computation. Transitions are bi-directional: towards more negative and positive divergence are gray and green, respectively. (A) Example of a selective change in the input. Here, unit 4 receives additional external input only if the network enters forbidden subspace 6 (see inset). This change forces the network to make a transition to a subspace with less negative divergence (green). As a result, the eventual solution is more likely to be subspace 15. (B) Example of continuously changing inputs (input noise). Notice how noise introduces additional transitions towards less negative divergence at all stages. However, the general gradient towards more negative divergence persists: overall, 26% of transitions made the divergence more positive.