{"ID":2864344,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23896","arxiv_id":"2509.23896","title":"A Computational Perspective on NeuroAI and Synthetic Biological Intelligence","abstract":"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.","short_abstract":"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 netw...","url_abs":"https://arxiv.org/abs/2509.23896","url_pdf":"https://arxiv.org/pdf/2509.23896v2","authors":"[\"Dhruvik Patel\",\"Md Sayed Tanveer\",\"Jesus Gonzalez-Ferrer\",\"Alon Loeffler\",\"Brett J. Kagan\",\"Mohammed A. Mostajo-Radji\",\"Ge Wang\"]","published":"2025-09-28T14:05:27Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.ET\",\"cs.NE\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}