Cognitive-Inspired Neural Architectures: Bridging Biological Intelligence and Deep Learning

Abstract

The pursuit of models that reflect the flexibility and interpretability of biological intelligence has gained renewed interest in recent years. Cognitive inspired neural architectures attempt to bridge the gap between deep learning and the mechanisms that support human cognition. These architectures draw on principles from neuroscience, cognitive psychology, and computational modeling to create systems that adapt, reason, and respond with greater autonomy. This article presents an extensive investigation of cognitive inspired neural models with emphasis on biologically grounded representations, hierarchical inference, dynamic memory integration, and multi agent coop eration. Through a unified methodology, architectural proposal, experimental benchmarks, and comparative evaluations, the study demonstrates how these models show promising advantages over conventional deep networks, especially in settings where flexibility, transparency, and adaptation are essential. Prior research studies across cognitive architecture, neural reasoning, sensor driven learning, communication systems, medical analytics, and multi agent coordination support the theoretical and experimental claims.