13.6 Glocal Memory 269 The result of these modifications to the ordinary Hopfield net, is a Hopfield net that contin- ually maintains a set of key neurons, each of which individually represents a certain attractor of the net. Note that these key neurons — in spite of being “symbolic” in nature — are learned rather than preprogrammed, and are every bit as adaptive as the attractors they correspond to. Fur- thermore, if a key neuron is removed, the glocal Hopfield net algorithm will eventually learn it back, so the robustness properties of Hopfield nets are retained. The results of experimenting with glocal Hopfield nets of this nature are summarized in [GPI° 10]. We studied Hopfield nets with connectivity around .1, and in this context we found that glocality e slightly increased memory capacity ® massively increased the rate of convergence to the attractor, i.e. the speed of recall However, probably the most important consequence of glocality is a more qualitative one: it makes it far easier to link the Hopfield net into a larger system, as would occur if the Hopfield net were embedded in an integrative AGI architecture. Because a neuron external to the Hopfield net may now link to a memory in the Hopfield net by linking to the corresponding key neuron. 13.6.4 Neural-Symbolic Glocality in CogPrime In CogPrime, we have explicitly sought to span the symbolic/emergentist pseudo-dichotomy, via creating an integrative knowledge representation that combines logic-based aspects with neural-net-like aspects. As reviewed in Chapter 6 above, these function not in the manner of multimodular systems, but rather via using (probabilistic) truth values and (attractor neural net like) attention values as weights on nodes and links of the same (hyper) graph. The nodes and links in this hypergraph are typed, like a standard semantic network approach for knowl- edge representation, so they’re able to handle all sorts of knowledge, from the most concrete perception and actuation