Categorization, Memory, small-world networks and neural architecture
In the last post I had wondered about the clustering based solution to categorization and how they may also inform us about how memory (semantic variety) is stored in brain, as semantic memory is best modeled by an associational or confectionist network.
Thus, a semantic memory based on clustering models may consist of associations between clusters or categories of information. For example one cluster may correspond to the names of countries and another to name of cities. A particular type of connection or association between these two clusters may map a relation of —-IS A CAPITAL OF —- type where for example the fact that Paris is the capital of France is stored. For this knowledge to exist, one has to have prior notions of France is a Country and Paris is a City and on top of that an associational relation between the individual entities France and Paris belonging to particular clusters.
Much of this would be more apparent once relational models of categorization are also covered. For now let us assume that (semantic) memory itself may consist of clusters of neurons that are also interconnected. Interestingly one such neural architecture, that has also been able to simulate short-term memory has been the small-world network model. In this a large number of nodes (neurons ) are connected by edges (synapses) as in a typical random graph. These small-world networks are special in the sense that they have high clustering coefficients and low mean path length. Translated in English, this means they exhibit more than chance clustering (to enhance local processing) as well as display a small value of smallest mean path length (reflecting ease of global processing).
It is intriguing thta in the short term memory model using small-world networks simulation, the researchers found that the model could exhibit bistability, which may be crucial for memory formation. In bistability, the cluster or functional region corresponding to a particular memory can be in two states, depending on an input variable. Thus, a pulse (direction of attention) can activate/ deactivate a memory.
Crucially, it can be hypothesized that as the small-world network model of memory/ categorization is good for local-global processing as well as reflective of the actual brain and AI simulation architectures, the entire brain is a small-world network adequately categorizing and representing the sensory, motor and cognitive information and processing them.
A recent MEG based study has established the fact that the small-world network topology exists in functional sphere in the brain at all oscillatory levels (crucial for binding) and that seems very promising.
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