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The Artificial Neural Networks (ANNs) have been originally designed tofunction like a biological neural network, but does an ANN really work in thesame way as a biological neural network? As we know, the human brain holdsinformation in its memory cells, so if the ANNs use the same model as ourbrains, they should store datasets in a similar manner. The most popular typeof ANN architecture is based on a layered structure of neurons, whereas a humanbrain has trillions of complex interconnections of neurons continuouslyestablishing new connections, updating existing ones, and removing theirrelevant connections across different parts of the brain. In this paper, wepropose a novel approach to building ANNs which are truly inspired by thebiological network containing a mesh of subnets controlled by a centralmechanism. A subnet is a network of neurons that hold the dataset values. Weattempt to address the following fundamental questions: (1) What is thearchitecture of the ANN model? Whether the layered architecture is the mostappropriate choice? (2) Whether a neuron is a process or a memory cell? (3)What is the best way of interconnecting neurons and what weight-assignmentmechanism should be used? (4) How to incorporate prior knowledge, bias, andgeneralizations for features extraction and prediction? Our proposed ANNarchitecture leverages the accuracy on textual data and our experimentalfindings confirm the effectiveness of our model. We also collaborate with theconstruction of the ANN model for storing and processing the images.

Rethinking the Artificial Neural Networks: A Mesh of Subnets with a Central Mechanism for Storing and Predicting the Data