Concepts are of great value to humans because they are one of the building blocks of our
cognitive processes. They are involved in cognitive functions that are fundamental in decision
making such as classification and also capacitate us for contextual comprehension. By definition,
a concept refers to an idea or a combination of several ideas. In a computational context, a
concept can be a feature or a set of features. An individual concept is referred to as a concrete
concept, whereas a generalized form of a set of concepts can be perceived as an abstract concept.
Computational concepts can be characterized in three broad categories; i.e. symbolic (e.g.
Adaptive Control of Thought based approach), distributed (e.g. Neural Networks) and spatial
(e.g. Conceptual Space) representations. CLARION, a cognitive architecture, is an example
of a hybrid computational framework that combines symbolic and distributed representations.
Moreover, the symbolic, distributed, spatial and hybrid representations are mostly used on
representing concrete concepts, whereas the notion of an abstract concept is rarely explored.
In this thesis, we propose a computational cognitive model, named Regulated Activation Net-
work (RAN), capable of dynamically forming the abstract representations of concepts and to
unify the qualities of spatial, symbolic and distributed computational approaches. Our model
aims to simulate the cognitive processes of concept learning, creation and recall. In particular,
the RAN’s modeling has three learning mechanisms where two perform inter-layer learning that
helps in propagating activations from an input-to-output layer and vice versa. The third pro-
vides an intra-layer learning that is used to emulate regulation mechanism, which is inspired by
biological Axoaxonic synapse where one node in a layer induces excitatory, neutral or inhibitory
activation to other nodes in the layer. In this research, two different types of abstract con-
cepts are modeled: first, the convex abstract concepts where the geometrical convexity among
the concrete concepts was exploited to create the abstract concept; second, the non-convex ab-
stract concepts where the similarity relationships among the convex abstract concepts were used
to capture non-convexity and model it. The RAN uniquely unifies the qualities of symbolic,
distributed and spatial conceptual representation, where the model has a dynamic topology,
simulates cognitive process like learning and concept creation and performs machine learning
Experiments with 11 benchmarks demonstrated the classification capability of RAN’s modeling
and provided a proof-of-concept of convex and non-convex abstract concept modeling. In these
experiments, the study has shown that RAN performed satisfactorily when compared with five
different classifiers. One of the datasets was used to model the active and inactive states of
three students. Further, the results of this model of students were analyzed statistically to
infer students’ psychological and physiological conditions. The recall experiments with RAN
demonstrated the cued recall blend retrieval of abstract concepts. Besides cognitive function
simulation and machine learning, the RAN’s model was also useful in the data analysis task.
In one of the experiments, a RAN’s model was developed to have 7 layers showing dimension
reduction and expansion operations. Additionally, the data visualization of the 1st, 3rd, and
5th layers displayed how deep data analysis with the RAN model unearth the complexities in
The research work involved the study of topics from the fields of Mathematics, Computational
Modeling, Psychology, Cognition, and Neurology. Based upon the results of all the experiments
and analogical reasoning of RAN’s modeling processes, the hypotheses of the research work
were demonstrated. The abstract concept modeling was substantiated through classification
experiments, whereas the simulations of concept creation, learning, activation propagation, and
recall were justified through analogy and empirical outcomes. The research work also helped in
discovering new challenges, such as temporal learning and simulation of the cognitive process
of forgetting, which will be taken as research projects in the future.