Beyond Imitation: Neuroscience-Inspired Architectures for Reasoning, Memory, and Abstraction

Abstract

The intersection of artificial intelligence and neuroscience offers revolutionary potential for designing machines beyond existing constraints of pattern matching towards realizing true reasoning and comprehension. This work explores how insights from predictive coding, hippocampal episodic memory, and prefrontal executive control in the biological domain can be applied to hybrid architectures blending neural and symbolic computation. The suggested framework combines content-addressable memory systems for fast episodic encoding and access, goal-conditioned controllers acting over abstract program spaces, and self-supervised world models using predictive coding for counterfactual reasoning. Training schedules drawing on biological development switch between passive viewing and active searching, allowing systems to extract maximum information from sparse data without overfitting. Evaluation paradigms transcend standard accuracy measures to evaluate compositional generalization, causal reasoning, and transfer learning ability that distinguish true intelligence. The resulting architectures show radical advances in sample efficiency, needing orders of magnitude fewer training data than standard transformer models while generalizing better out-of-distribution. These developments imply that the integration of neuroscientific principles allows qualitatively different learning dynamics that reflect biological intelligence, providing avenues to artificial general intelligence that learns and reasons in essentially human-compatible means.