Australian startup Cortical Labs has unveiled the CL1, the world's first commercial biological computer that runs on living human brain cells, fusing lab-cultivated neurons from human stem cells with silicon to create a new form of AI called Synthetic Biological Intelligence that can learn and adapt faster than standard silicon-based AI while consuming significantly less energy.
Synthetic Biological Intelligence (SBI) represents a revolutionary fusion of biology and technology, integrating living human brain cells with electronic systems to create intelligence that's more dynamic and energy-efficient than traditional AI. Unlike conventional silicon-based systems, SBI leverages the natural learning capabilities of biological neurons, allowing them to form fluid neural networks that can adapt and learn at remarkable speeds12. The CL1 system from Cortical Labs features 59 electrodes arranged in a grid that provides a foundation for lab-grown neurons to develop connections, complete with life support systems including filtration pumps, temperature control, and gas mixing to maintain cellular health3.
SBI offers several key advantages over traditional AI, including significantly lower energy consumption (using approximately 1 kilowatt per rack compared to the massive power requirements of large language models), faster learning capabilities, and potential applications across multiple fields3. This technology is being commercialized through a "Wetware-as-a-Service" (WaaS) model, allowing researchers worldwide to conduct experiments on biological neural networks remotely3. While SBI systems like the CL1 demonstrate impressive computational abilities, developers emphasize that these systems are not conscious or self-aware, but rather specialized computing substrates that harness the natural information processing capabilities of human neurons34.
The integration of neurons with silicon chips represents the foundational technology behind biological computing systems like the CL1. These neuron-silicon hybrid circuits typically consist of two primary components: synapse blocks that receive and integrate spikes from other neurons, and soma blocks that handle signal integration and generate output action potentials.1 When biological neurons are cultured directly on silicon chips, they form functional networks where the chip can both stimulate the neurons through capacitors and record their activity via transistors.2 This two-way communication creates a true hybrid system where biological processing interfaces directly with electronic components.
Silicon chips designed for neuronal integration have become increasingly sophisticated, with recent advances enabling massive parallel recording capabilities. Harvard researchers developed a microhole electrode array capable of mapping over 70,000 synaptic connections from approximately 2,000 rat neurons on a single chip.3 These systems maintain neuronal health through integrated life support systems while leveraging the neurons' natural plasticity—their ability to adapt connections in response to new information—which is crucial for learning and memory functions.4 Unlike traditional computing architectures, these hybrid systems harness the inherent computational efficiency of biological neurons while providing precise electronic control and measurement capabilities.
The CL1's internal life support system is a sophisticated bioengineering achievement that maintains viable neurons for up to six months, addressing one of the key challenges in biological computing. This "body in a box" includes multiple components that mimic human physiological systems: pumps that function like the heart, waste filtration units similar to kidneys, and a specialized gas mixer that regulates carbon dioxide, oxygen, and nitrogen levels12. The system also maintains precise temperature control and circulates a nutrient-rich solution that provides essential nourishment to the neurons cultivated on the silicon chip34.
Operating with remarkable efficiency, the life support system requires minimal inputs and consumes just a few watts of power35. This energy efficiency is a significant advantage over traditional computing technologies, enabling extended research timelines while maintaining the biological integrity of the neural networks. The perfusion circuit component continuously filters waste products and circulates fresh nutrients, creating a stable microenvironment that allows the neurons to form connections and process information in ways that closely resemble natural brain function34. This technological breakthrough eliminates the need for animal testing while providing more relevant human data for applications in disease modeling and drug discovery65.
