Brain Inspired Cognitive Architecture of Hierarchical Distributed Model Based on Artificial Intelligence

The next generation of Artificial Intelligence (AI) will need to engage in social interactions with its users and encourage them that it can comprehend mental and emotional states. Several cutting-edge cognitive architectures have recently taken inspiration from the human brain, with researchers analyzing and contrasting them based on how they model the brain's neuronal mechanisms for feeling. This work classifies the Brain-inspired cognitive architecture (BICA) using Artificial Intelligence termed the Hierarchical Distributed Model (AI-HDM) for the computation of the human brain to determine novel knowledge based on the inputs sensed by sensory systems acquired from physical environments. The data are taken from the Human Memory and Cognition Kaggle Dataset. To understand the internal functioning and dynamic nature of the brain's neural processes, a semantic map is used to encompass a foundational set of motor abilities, perceptions, and emotions. Different facets of human behavior may be self-organized according to the patterns of neuronal activity seen during complicated decision-making processes. Both empirical and simulated results demonstrate that the suggested method may significantly influence BICA and outperforms more traditional frameworks in prediction accuracy. The experimental outcome demonstrates that the suggested Al-HDM model increases the efficiency ratio by 92.4%, understanding ability by 93.5%, active learning by 94.4% and reduced error rate of 9.6% compared to other existing models.