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We next analytically minimize probability flow to determine explicit networks achieving robust exponential storage.

McCulloch Pitts attractor networks minimizing probability flow can achieve robust exponential pattern storage.

Fig. 2 Learning critical networks with exponential memory by minimizing probability flow on few training patterns.

Fig. 3 Distribution of network parameters learned by minimizing probability flow (MPF) sharpens around three critical values.

Although we have focused on minimizing probability flow to learn parameters in our discrete neural networks, several other strategies exist.

Here, we discover such networks by minimizing probability flow, a recently proposed objective for estimating parameters in discrete maximum entropy models.

It follows that the optimal setting (7) for x minimizing probability flow gives robust storage (with a single parallel dynamics update) of all k-cliques for (p < 1/4).

This in turn can minimize probability of blocking and probability of false network assignment.