NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.
@article{arxiv.2509.23896,
title = {A Computational Perspective on NeuroAI and Synthetic Biological Intelligence},
author = {Dhruvik Patel and Md Sayed Tanveer and Jesus Gonzalez-Ferrer and Alon Loeffler and Brett J. Kagan and Mohammed A. Mostajo-Radji and Ge Wang},
journal= {arXiv preprint arXiv:2509.23896},
year = {2025}
}
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Submitted to Nature Communications, under "NeuroAI and Embodied Intelligence" collection