Synthetic biological intelligence (SBI) is used for engineered systems in which living biological neural networks are coupled to hardware and software for task-oriented information processing. The term overlaps with neuronal computing, wetware computing and organoid intelligence.
SBI is useful because it describes the complete engineered system and avoids implying independent intelligence in a culture or organoid. In practice, stimulation hardware, recording electronics, decoding software and environmental control are part of the machine.
What counts as SBI?
DishBrain is a canonical example: living neurons were connected to a virtual environment through a multielectrode array and a real-time feedback loop. Newer platforms expose similar biological networks through standardised hardware or remote software interfaces.
Why the term matters
The field spans neuroscience, tissue engineering, electrophysiology, machine learning, control theory and hardware engineering. A 2025 practical paper on starting an SBI laboratory makes that multidisciplinary burden explicit. Standardised platforms may reduce it by allowing more researchers to work on encoding, decoding and learning rules without maintaining every wet-lab component themselves.
SBI and organoid intelligence
Organoid intelligence concentrates on three-dimensional brain organoids. SBI is broader and can include dissociated two-dimensional neuronal cultures. The boundaries are still settling, so this site records the terminology used by each paper and allows overlapping labels where needed.