To understand the principles that underpin human-like intelligence in machines, it is essential to examine the neocortex, the brain region most closely associated with perception, abstraction, and complex thought. This chapter focuses specifically on the neocortex’s architecture and functionality. It begins by outlining four core properties that characterize neocortical processing: structural uniformity, invariant representation, hierarchical organization, and auto-associative memory. These properties enable the brain to recognize patterns, interpret sensory data, and generate contextually appropriate predictions. The discussion then turns to leading theoretical models of neocortical function, with particular attention to Jeff Hawkins’ frameworks. These models propose that the brain operates on shared learning principles across modalities and emphasize the importance of temporal memory, sensorimotor integration, and predictive processing. Subsequent sections examine how these biological insights contribute to the development of artificial intelligence. A central focus is Hierarchical Temporal Memory (HTM), a computational model inspired by the neocortex that replicates many of its structural and functional characteristics. HTM incorporates sparse distributed representations, online sequence learning, and hierarchical inference, providing a biologically plausible foundation for machine learning. The chapter concludes with a summary of key concepts, paving the way for deeper exploration of brain-inspired AI systems.