## 1 Introduction
Some techniques exhibit dynamic and evolving behavior while performing computational operations, such as evolving neural networks by using their genotype-phenotype mapping of cells [13]. The proposed model emulates the behavior of the dynamic creation of abstract concepts by evolving the computational model upon identifying different groups in the data. This article proposes a computational method named Regulated Activation Network (RAN) which unifies the virtues of symbolic, distributed, and spatial representations to represent concepts (both concrete and abstract). RAN has a graph-based topology hence it is distributed, every node in the graph (network) identifies an entity, therefore it is symbolic, and every node (or entity) is viewed in an n-dimensional feature space, hence it is also spatial. The spatial view of concepts as points in multidimensional geometric feature space (see Figure 1 for six-dimensional view of concepts) is inspired by the theory of conceptual spaces [14]. The RAN's modeling has an evolving topology that enables it to build a model depicting a hierarchy of concepts. The geometrical associations among concepts aid in determining the convex abstract concepts. Further, the representatives (nodes) of the abstract concepts form a new layer dynamically, where each node acts as a convex abstract concept representative for the underlying category. Symbolically, the concepts at (relatively) lower levels in the hierarchy are identified as concrete concepts and the concepts at (relatively) higher levels are seen as abstract concepts. The model generation process with RAN and the three cognitive functions (i.e., concept creation, learning and activation propagation) are simulated using a Toy-data problem. The deep hierarchy generation, automatic generic concept modeling simulations are performed using two University of California Irvine (UCI) benchmarks: IRIS data; and IoT data from smartphone sensors. The application of RAN as a classifier is reported along with the proof of concept of classification using eight UCI benchmark datasets. The generated models were evaluated using metrics precision, recall, F1-score, accuracy, and Receiver Operating Characteristic (ROC) curve analysis. The article also reports the RANs classification and feature comparison with five machine learning techniques, Multilayer Perceptron (MLP) [15], Logistic Regression (LR) [16], K Nearest Neighbors (K-NN) [17], Stochastic Gradient Descent (SGD) [18] and Restrict Boltzmann Machine [9] pipelined with Logistic Regression (RBM+). The article is organized in the following order; Section 2 puts forward the work closely related to abstract concept representation and models with evolving topology. Section 3 describes the background associated with principles, theories, and motivations for RAN modeling. RANs methodology is detailed using Toy-data in Section 4. Section 5 shows the experiments with two datasets acquired from UCI machine learning repository to exhibit (1) flexibility in choosing a suitable concept identifier, (2) building a deep hierarchy of abstract concepts, (3) automatic association of input-labels to their respective abstract concept nodes. Section 6 provides RAN comparisons with five classifiers and proof of concept with eight benchmark datasets. At last, Section 7 summarizes and concludes the article with remarks over ongoing and future work.