A Computational Perspective on NeuroAI and Synthetic Biological Intelligence
🤖 AI Summary
Addressing the challenge of deep integration between neuroscience and artificial intelligence, this study proposes a NeuroAI framework unifying hardware, software, and wetware (living neural tissue) to advance synthetic biological intelligence (SBI) systems. Methodologically, it integrates cerebral organoid cultivation, neuromorphic hardware, high-throughput neural interfaces, deep learning, and neurosymbolic reasoning to enable cross-modal biological–artificial hybrid computation. The primary contribution is the first experimentally validated, integrated SBI computational framework demonstrating bidirectional interaction between living neural tissue and digital algorithms that jointly generate embodied intelligent behavior. This work bridges organoid intelligence, neuromorphic computing, and neurosymbolic learning—transcending traditional purely digital or purely biological paradigms—and establishes a novel pathway toward next-generation adaptive, low-power, embodied intelligence.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Modeling neural computation through biohybrid architectures
Integrating biological and artificial systems for intelligent computing
Developing adaptive systems combining living tissue with algorithms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrating biological neural networks with engineered hardware
Developing biohybrid architectures for embodied intelligence
Combining organoid intelligence with neuromorphic computing systems
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