78 4 Brief Survey of Cognitive Architectures interacts with other subsystems in the brain much in the manner that the symbolic and neural components of a symmetric high-interaction neural-symbolic system interact. Neuroscience speculations aside, however, our key conjecture regarding neural-symbolic in- tegration is that this sort of neural-symbolic system presents a promising direction for artificial general intelligence research. In Chapter 26 of Volume 2 we will give a more concrete idea of what asymmetric high-interaction hybrid neural-symbolic architecture might look like, explor- ing the potential for this sort of hybridization between the OpenCogPrime AGI architecture (which is heavily symbolic in nature) and hierarchical attractor neural net based architectures such as DeSTIN. 4.5 Globalist versus Localist Representations Another interesting distinction, related to but different from “symbolic versus emergentist” and “neural versus symbolic”, may be drawn between cognitive systems (or subsystems) where memory is essentially global, and those where memory is essentially local. In this section we will pursue this distinction in various guises, along with the less familiar notion of glocal memory. This globalist/localist distinction is most easily conceptualized by reference to memories corresponding to categories of entities or events in an external environment. In an AI system that has an internal notion of “activation” — i.e. in which some of its internal elements are more active than others, at any given point in time — one can define the internal image of an external event or entity as the fuzzy set of internal elements that tend to be active when that event or entity is presented to the system’s sensors. If one has a particular set S of external entities or events of interest, then, the degree of memory localization of such an AI system relative to S may be conceived as the percentage of the system’s internal elements that have a high degree of member