260 13 Local, Global and Glocal Knowledge Representation
cated acts of perception. In general-evolution language, what is posited here is that organisms like humans contain chemical signals that signify organism-level success of various types, and that these signals serve as a “fitness function” correlating with evolutionary fitness of neuronal maps.
In Neural Darwinism and his other related books and papers, Edelman goes far beyond this crude sketch and presents neuronal group selection as a collection of precise biological hypothe- ses, and presents evidence in favor of a number of these hypotheses. However, we consider that the basic concept of neuronal group selection is largely independent of the biological particular- ities in terms of which Edelman has phrased it. We suspect that the mutation and selection of “transformations” or “maps” is a necessary component of the dynamics of any intelligent system.
As we will see later on (e.g. in Chapter 42 of Part 2, this business of maps is extremely important to CogPrime. CogPrime does not have simulated biological neurons and synapses, but it does have Nodes and Links that in some contexts play loosely similar roles. We sometimes think of CogPrime Nodes and Links as being very roughly analogous to Edelman’s neuronal clusters, and emergent intercluster links. And we have maps among CogPrime Nodes and Links, just as Edelman has maps among his neuronal clusters. Maps are not the sole bearers of meaning in CogPrime, but they are significant ones.
There is a very natural connection between Edelman-style brain evolution and the ideas about cognitive evolution presented in Chapter 3. Edelman proposes a fairly clear mechanism via which patterns that survive a while in the brain are differentially likely to survive a long time: this is basic Hebbian learning, which in Edelman’s picture plays a role between neuronal groups. And, less directly, Edelman’s perspective also provides a mechanism by which intense patterns will be differentially selected in the brain: because on the level of neural maps, pattern intensity corresponds to the combination of compactness and functionality. Among a number of roughly equally useful maps serving the same function, the more compact one will be more likely to survive over time, because it is less likely to be disrupted by other brain processes (such as other neural maps seeking to absorb its component neuronal groups into themselves). Edelman’s neuroscience remains speculative, since so much remains unknown about human neural structure and dynamics; but it does provide a tentative and plausible connection between evolutionary neurodynamics and the more abstract sort of evolution that patternist philosophy posits to occur in the realm of mind-patterns.
13.6 Glocal Memory
A glocal memory is one that transcends the global/local dichotomy and incorporates both aspects in a tightly interconnected way. Here we make the glocal memory concept more precise, and describe its incarnation in the context of attractor neural nets (which is similar to its incarnation in CogPrime, to be elaborated in later chapters). Though our main interest here is in glocality in CogPrime, we also suggest that glocality may be a critical property to consider when analyzing human, animal and AI memory more broadly.
The notion of glocal memory has implicitly occurred in a number of prior brain theories (without use of the neologism “glocal”), e.g. [Cal96] and [Goe01], but it has not previously been explicitly developed. However the concept has risen to the fore in our recent AI work and so we have chosen to flesh it out more fully in [HGO8], [GPI~ 10] and the present section.
HOUSE_OVERSIGHT_013176