Neuromorphic Computing: Foundations and the Case for Principle-Level Integration in AI Systems Part I of IV

Synchronous with technological progress in Artificial Intelligence (AI), demands on underlying computing architectures are increasing rapidly. Neuromorphic computing (NMC) offers novel approaches to energy-efficient processing of time-dependent information and enables low-latency, sensor-proximal interaction between environment, humans, and machines. Yet its relevance for industry and AI development extends beyond dedicated neuromorphic platforms: core neuromorphic principles, including event-driven processing, temporal coding, and co-located memory and computation, are increasingly being absorbed into mainstream AI hardware and software architectures. Rather than emerging as a unified standalone paradigm in the near term, NMC appears poised to reshape AI systems through gradual, principle-level integration and hybridization with classical approaches. This essay, the first in a four-part series, introduces the technical foundations of NMC and develops the above argument through a structured comparison of biological information processing, classical artificial neural networks (ANNs), and spiking neural networks (SNNs). Using visual information processing as a concrete illustrative example, we highlight the distinct operating principles of each paradigm and begin to contextualize their respective limitations and potentials. We discuss the hypothesis that the broader impact of NMC will unfold through hybridization rather than replacement and consider implications for industrial actors and technology transfer. The series aims to make the potential of NMC accessible and tangible for product development and process innovation. It is offered as a contribution to an ongoing cross-disciplinary dialogue, with particular attention to the European research and innovation ecosystem and the strategic opportunity it represents for technology sovereignty and industrial competitiveness.