Neural Modeling Fields - Neural Modeling Fields Hierarchical Organization

Neural Modeling Fields Hierarchical Organization

Above, a single processing level in a hierarchical NMF system was described. At each level of hierarchy there are input signals from lower levels, models, similarity measures (L), emotions, which are defined as changes in similarity, and actions; actions include adaptation, behavior satisfying the knowledge instinct – maximization of similarity. An input to each level is a set of signals X(n), or in neural terminology, an input field of neuronal activations. The result of signal processing at a given level are activated models, or concepts m recognized in the input signals n; these models along with the corresponding instinctual signals and emotions may activate behavioral models and generate behavior at this level.

The activated models initiate other actions. They serve as input signals to the next processing level, where more general concept-models are recognized or created. Output signals from a given level, serving as input to the next level, are the model activation signals, a_m, defined as

a_m = ∑_n=1..N f(m|n).

The hierarchical NMF system is illustrated in Fig. 2. Within the hierarchy of the mind, each concept-model finds its “mental” meaning and purpose at a higher level (in addition to other purposes). For example, consider a concept-model “chair.” It has a “behavioral” purpose of initiating sitting behavior (if sitting is required by the body), this is the “bodily” purpose at the same hierarchical level. In addition, it has a “purely mental” purpose at a higher level in the hierarchy, a purpose of helping to recognize a more general concept, say of a “concert hall,” a model of which contains rows of chairs.

From time to time a system forms a new concept or eliminates an old one. At every level, the NMF system always keeps a reserve of vague (fuzzy) inactive concept-models. They are inactive in that their parameters are not adapted to the data; therefore their similarities to signals are low. Yet, because of a large vagueness (covariance) the similarities are not exactly zero. When a new signal does not fit well into any of the active models, its similarities to inactive models automatically increase (because first, every piece of data is accounted for, and second, inactive models are vague-fuzzy and potentially can “grab” every signal that does not fit into more specific, less fuzzy, active models. When the activation signal a_m for an inactive model, m, exceeds a certain threshold, the model is activated. Similarly, when an activation signal for a particular model falls below a threshold, the model is deactivated. Thresholds for activation and deactivation are set usually based on information existing at a higher hierarchical level (prior information, system resources, numbers of activated models of various types, etc.). Activation signals for active models at a particular level { a_m } form a “neuronal field,” which serve as input signals to the next level, where more abstract and more general concepts are formed.