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The persistent endeavour in advancing artificial intelligence requires moving from purely classical computational paradigms, driven by the inherent limitations of conventional architectures in emulating human-like cognition and adaptability. Biological neural systems, particularly the brain, offer a compelling blueprint for emergent intelligence, characterized by massive parallelism, energy efficiency, and inherent plasticity. This paper presents a comprehensive system-level framework for understanding neuro-computational architectures, classifying them based on the interplay of classical and neural hardware and software components. A taxonomy is established, identifying four distinct categories: Classical Software on Classical Hardware (CSCH), Neural Software on Classical Hardware (NSCH), Classical Software on Neural Hardware (CSNH), and Neural Software on Neural Hardware (NSNH). This framework highlights how neuro-computational systems leverage brain-inspired principles to overcome limitations of traditional computing, fostering the development of more robust, adaptive, and intelligent cognitive architectures.

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A Neuro-Computational Architecture Taxonomy to Bridge Biological and Artificial Intelligence

  • 会议论文

  • 发表日期：

    2026年

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  • Salvatore Distefano,
  • Max Talanov

摘要

The persistent endeavour in advancing artificial intelligence requires moving from purely classical computational paradigms, driven by the inherent limitations of conventional architectures in emulating human-like cognition and adaptability. Biological neural systems, particularly the brain, offer a compelling blueprint for emergent intelligence, characterized by massive parallelism, energy efficiency, and inherent plasticity. This paper presents a comprehensive system-level framework for understanding neuro-computational architectures, classifying them based on the interplay of classical and neural hardware and software components. A taxonomy is established, identifying four distinct categories: Classical Software on Classical Hardware (CSCH), Neural Software on Classical Hardware (NSCH), Classical Software on Neural Hardware (CSNH), and Neural Software on Neural Hardware (NSNH). This framework highlights how neuro-computational systems leverage brain-inspired principles to overcome limitations of traditional computing, fostering the development of more robust, adaptive, and intelligent cognitive architectures.