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A Computational Perspective on NeuroAI and Synthetic Biological Intelligence
Dhruvik Patel1,2,3, Md Sayed Tanveer1,2, Jesus Gonzalez-Ferrer4
1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, 12180, NY, USA.
Arxiv
|November 24, 2025
Summary
NeuroAI merges neuroscience and artificial intelligence to create intelligent systems inspired by brain function. This field, including synthetic biological intelligence (SBI), explores biohybrid systems for advanced computation and embodied intelligence.
Area of Science:
- Neuroscience and Artificial Intelligence (AI)
Background:
- NeuroAI integrates neuroscience principles with AI for intelligent system design.
- Synthetic Biological Intelligence (SBI) combines biological neural network learning with engineered systems.
Purpose of the Study:
- To review the NeuroAI landscape, focusing on hardware, software, and wetware domains.
- To outline computational frameworks integrating biological and non-biological systems.
- To highlight advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning.
Main Methods:
- Organizing the NeuroAI field into hardware, software, and wetware.
- Outlining computational frameworks for bio-digital integration.
- Reviewing recent advancements in key subfields.
Main Results:
- Identification of three interacting domains: hardware, software, and wetware.
- Frameworks for integrating biological and non-biological computational systems.
- Progress in organoid intelligence, neuromorphic computing, and neuro-symbolic AI.
Conclusions:
- NeuroAI is advancing toward novel systems computing via living neural tissue and digital algorithms.
- Synthetic Biological Intelligence (SBI) offers a platform for modeling neural computation and biohybrid architectures.
- The convergence of these fields promises new forms of embodied intelligence and computation.
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