**Published** : 12/03/2025 **Last edited** : 12/03/2025 # Synthetic Biological Intelligence : The Fusion of Neurons and Silicon The emergence of Synthetic Biological Intelligence (SBI) represents a revolutionary frontier in computing technology, where biological neural systems merge with digital computing to create intelligent systems that learn and adapt with unprecedented efficiency. Recent commercial developments by Australian company Cortical Labs [^1] have brought this technology from theoretical research to tangible products, offering the world's first "biological computer" powered by human brain cells. This technology demonstrates substantial advantages over traditional computing systems, particularly in energy efficiency and learning capabilities, while raising profound questions about consciousness, ethics, and the blurring boundaries between silicon and biological computing. The biocomputing revolution has begun with early applications in drug discovery and disease modeling, potentially expanding to broader computational tasks as the technology matures. ## The Emergence of Synthetic Biological Intelligence Synthetic Biological Intelligence represents a paradigm shift in computing technology, integrating living neural cultures with digital hardware to create new forms of computational systems. Unlike traditional artificial intelligence that relies solely on silicon processors and algorithms, SBI harnesses the natural computational power of biological neurons grown directly on custom-designed chips. This approach is not merely a technical curiosity but potentially a solution to fundamental limitations facing conventional computing. ```mermaid mindmap root((Traditional vs. Biological Computing)) Traditional Computing Silicon-Based Processors Digital Logic Algorithm-Based Learning High Power Consumption Requires Extensive Data Biological Computing Neural Networks Biological Adaptation Experience-Based Learning Energy Efficient Learns from Limited Examples ``` The concept of biocomputing has evolved over several decades, but only recently has the technology advanced sufficiently to produce viable computing systems. The development of SBI has been enabled by three key technological breakthroughs: advances in electrophysiology allowing precise reading and stimulation of neural activity, improvements in artificial intelligence algorithms that can interpret biological signals, and progress in cultivating cerebral [[organoids]] and neural tissues in laboratory settings. These converging technologies have created the foundation for practical biocomputing devices that can process information through biological rather than purely electronic means. Traditional computing faces two significant challenges that SBI potentially addresses. First is the energy consumption problem – modern supercomputers require enormous amounts of power. While the human brain operates on approximately 20 watts of power, comparable supercomputers demand megawatts of electricity. Second is the ability to learn from limited data – biological neural systems excel at making complex decisions based on minimal information, a capability that traditional AI systems struggle to replicate despite increasing computational power. ## Technology Behind SBI: How It Works The fundamental architecture of Synthetic Biological Intelligence systems involves growing real neurons on specialized silicon chips that facilitate bidirectional communication between the biological and electronic components. In Cortical Labs' implementation, human-induced pluripotent stem cells (hiPSCs) are transformed into neurons and placed onto high-density multielectrode arrays, creating a hybrid biological-electronic computing system. ```mermaid flowchart TD subgraph "Digital Component" A[Digital Input] B[biOS Interface] C[Electrical Stimulation] E[Electrical Response] F[Signal Interpretation] G[Digital Output] end subgraph "Biological Component" D[Neural Network] end subgraph "Maintenance Systems" H[Life Support Systems] end A --> B B --> C C --> D D --> E E --> F F --> G H --> D ``` These systems employ a sophisticated life-support infrastructure to maintain the viability of the neural cultures. As described by Cortical Labs CEO Hon Weng Chong, "We have pumps that function like a heart, waste management, feeding reservoirs, and filtration systems akin to kidneys. Additionally, there's a gas mixer for carbon dioxide, oxygen, and nitrogen" [^2]. This complex arrangement ensures the neurons remain functional within their artificial environment for extended periods – reportedly up to six months in the case of Cortical's CL1 system. The interface between the biological and digital components is managed through what Cortical Labs calls their Biological Intelligence Operating System (biOS). This system creates a virtual environment and transmits information to the neurons through electrical stimulation via the electrode array. The neural network processes this information and responds with its own electrical signals, which are then interpreted by the digital system. This bidirectional communication creates a closed-loop system where the biological component can learn and adapt based on the inputs it receives. The learning mechanism in these systems capitalizes on the fundamental property of neurons to seek predictable outcomes. Research by Cortical Labs has shown that neurons will adapt their networks to produce energy-efficient, predictable responses and avoid behaviors that generate chaotic electrical patterns. This inherent drive toward predictability forms the basis for training these biological computing systems to perform specific tasks. ## Cortical Labs' CL1: The First Commercial Biocomputer In March 2025, Cortical Labs officially launched the CL1, marketed as the world's first commercially available code-deployable biological computer. This landmark product represents the culmination of six years of research and development, transforming the concept of biocomputing from theoretical possibility to commercial reality. The CL1 system contains hundreds of thousands of laboratory-created neurons derived from human blood cells converted into stem cells. ![[Capture d’écran, le 2025-03-12 à 13.00.06.png]] The hardware configuration of the CL1 consists of neurons arranged on a 59-electrode array made of metal and glass, integrated into a comprehensive life-support unit connected to specialized software systems. This integration allows for real-time interaction between the neurons and computing environment, creating what Cortical Labs describes as a "high-performance closed-loop system where genuine neurons engage with software in real time". Priced at approximately $35,000, the CL1 targets research institutions and pharmaceutical companies, though Cortical Labs also offers a more accessible "Wetware-as-a-Service" option allowing users to access computational power from these neural systems through the cloud. This cloud-based option significantly expands the potential user base by eliminating the need for specialized equipment and expertise in maintaining biological systems. The CL1 builds upon earlier demonstrations of biocomputing capabilities. In 2021, Cortical Labs created a system in which neurons grown on a chip successfully learned to play the game Pong [^4], demonstrating the ability of these neural networks to sense the position of the game's electronic ball and control a virtual paddle. This breakthrough experiment revealed the learning potential of integrated neuron-silicon systems, showing how they could adapt to novel tasks without explicit programming. ## Scientific Foundations and Research The development of Synthetic Biological Intelligence is grounded in multiple scientific disciplines including synthetic biology, neuroscience, and computer science. Scientific research in this area has explored various approaches to creating computational systems using biological components, from simple neural networks to more complex organoid structures. ```mermaid graph TB A[1940s: Neural Network Theory] --> B[1970s: First Brain-Computer Interfaces] B --> C[1990s: Neural Culture Experiments] C --> D[2000s: Multielectrode Arrays] D --> E[2010s: Organoid Development] E --> F[2021: Neurons Playing Pong] F --> G[2025: CL1 Commercial Release] style A fill:#f5f5f5,stroke:#333,stroke-width:2px style B fill:#f5f5f5,stroke:#333,stroke-width:2px style C fill:#e6f3ff,stroke:#333,stroke-width:2px style D fill:#e6f3ff,stroke:#333,stroke-width:2px style E fill:#d1e7ff,stroke:#333,stroke-width:2px style F fill:#b8dbff,stroke:#333,stroke-width:2px style G fill:#9fcfff,stroke:#333,stroke-width:2px ``` A foundational concept in this field is the development of what researchers call "metabolic perceptrons" for neural computing in biological systems. As described in Nature Communications[^3], this approach involves engineering simple neural networks in biological systems that can perform classifications based on the concentrations of input metabolites. While these systems do not replicate the complexity of Cortical Labs' neuron-based computers, they demonstrate the broader potential for integrating computational functions into biological substrates. The integration of neural cultures with digital computing represents a more advanced implementation of biocomputing. In research published in Biotechnology Advances, Kagan and colleagues [^4] outline how this integration has enabled the early development of Synthetic Biological Intelligence, emphasizing the advantages of biological neural systems in certain information processing tasks. Their research provides a framework for understanding both the technological underpinnings and ethical considerations of SBI development. Another related approach is Organoid Intelligence (OI), which uses three-dimensional cultures of brain tissue (cerebral organoids) to create biocomputing systems. As described in Frontiers in Science [^5], these organoids can mimic the structure and main functions of human brains, potentially allowing researchers to harness the computational efficiency of biological neural networks while overcoming the energy limitations of traditional computing. This approach represents a different implementation of the same fundamental concept: using biological neural systems for computation. ## Applications and Potential of SBI The initial applications for Synthetic Biological Intelligence systems are primarily focused on scientific research, particularly in healthcare and medicine. The CL1 system shows particular promise for drug discovery and disease modeling, allowing researchers to test potential treatments on biological neural networks that respond more naturally than silicon-based simulations. ```mermaid mindmap root((SBI Applications)) Healthcare Drug Discovery Disease Modeling Personalized Medicine Computing Hybrid AI Systems Energy-Efficient Computing Pattern Recognition Research Neuroscience Studies Intelligence Research Brain-inspired Computing Future Possibilities Neural Interfaces Advanced Robotics Novel Computing Paradigms ``` In pharmaceutical research, these systems could significantly accelerate drug development by providing more accurate predictions of how neural tissues will respond to potential therapeutic compounds. This could reduce the time and cost of bringing new treatments to market, particularly for neurological conditions where traditional models often fall short. The biological component provides a more realistic testing environment than purely computational simulations, potentially reducing the gap between laboratory results and clinical outcomes. Beyond medical applications, SBI systems demonstrate potential for advancing artificial intelligence by creating hybrid systems that combine the learning efficiency of biological networks with the speed and reliability of digital computation. As Dr. Kagan of Cortical Labs explained, "If you have 120 CL1s, you can conduct highly controlled experiments to pinpoint the factors that contribute to the emergence of intelligence". This research direction could yield insights into the fundamental nature of intelligence and learning, potentially informing new approaches to artificial intelligence development. The energy efficiency of biocomputing represents another significant advantage, particularly as traditional computing approaches physical and energetic limitations. While current supercomputers capable of brain-like performance require megawatts of power, biological systems achieve comparable computational capabilities with orders of magnitude less energy. This efficiency could make biocomputing particularly valuable for applications where energy consumption is a limiting factor. ## Ethical Considerations and Challenges The development of Synthetic Biological Intelligence raises profound ethical questions that researchers and developers must address. These range from practical concerns about biological safety to deeper philosophical questions about consciousness and the moral status of these hybrid systems. As noted by Kagan and colleagues, these ethical considerations must be explored in detail to ensure that SBI technology can be "both researched and applied responsibly". One primary concern is whether the neural networks in these systems could potentially develop some form of consciousness or awareness. Some scientists have raised questions about whether neurons cultivated in laboratory settings might eventually "develop a consciousness or a comprehension of their state". While current systems are far from achieving anything resembling human consciousness, the theoretical possibility necessitates careful ethical consideration as the technology advances. Safety and containment represent more immediate practical concerns. Biological computing systems require stringent containment protocols to prevent contamination or unintended release of biological materials. Cortical Labs addresses this through their laboratory design, which includes Physical Containment Level 2 (PC2) protocols combining computer hardware with traditional biological specimens and equipment. The source and nature of the biological materials used in these systems also raise ethical questions. Cortical Labs uses induced pluripotent stem cells (IPSCs) derived from blood samples, which are reprogrammed into neurons for their systems. This approach avoids some of the ethical concerns associated with embryonic stem cells but still requires careful consideration of consent and privacy issues related to the original cell donors. In recognition of these ethical dimensions, companies like Cortical Labs report working with ethics specialists to address potential concerns. This collaboration between technical developers and ethics experts represents a crucial approach to ensuring that the technology develops in a responsible manner that respects both scientific advancement and ethical boundaries. ## Philosophical Implications of SBI The emergence of Synthetic Biological Intelligence forces us to reconsider fundamental philosophical questions about the nature of intelligence, consciousness, and the boundaries between biological and artificial systems. These hybrid computing systems blur traditional distinctions between "natural" and "artificial" intelligence, creating new categories that challenge our existing conceptual frameworks. The development of SBI systems raises profound questions about what constitutes intelligence and learning. Unlike traditional AI systems that rely on algorithms and vast datasets, these biological computing systems learn through inherent neurological processes more closely resembling natural intelligence. This distinction challenges us to reconsider whether intelligence is fundamentally tied to biological processes or can be fully replicated in silicon-based systems. More provocatively, SBI raises questions about consciousness and the potential moral status of engineered neural networks. While current systems are far too simple to manifest anything resembling human consciousness, their continued development forces us to consider at what point, if any, such systems might develop properties that would warrant moral consideration. This consideration becomes particularly important as researchers work to scale up these systems with "more complex and durable structures that are enriched with cells and genes associated with learning"[^5]. The hybridization of biological and silicon-based computing also raises questions about human enhancement and the potential future integration of similar technologies with the human nervous system. While current applications focus on standalone computing systems, the underlying technologies could potentially inform developments in neural interfaces and brain-computer integration, raising additional ethical and philosophical questions about human identity and autonomy. As we stand at the beginning of this biocomputing revolution, these philosophical questions remain largely theoretical. However, the rapid advancement of the technology necessitates proactive consideration of these deeper implications. As noted by researchers in the field, there is a need for an "embedded ethics" approach to ensure that organoid intelligence and related technologies develop "in an ethically and socially responsive manner" [^5]. This integration of ethical consideration with technological development will be essential as we navigate the profound philosophical questions raised by the emergence of Synthetic Biological Intelligence. ## Sources [^1]: Cortical Labs. (n.d.). Official website. [https://corticallabs.com/](https://corticallabs.com/) [^2]: Gizmodo. (n.d.). This $35,000 Computer Is Powered By Trapped Human Brain Cells. [https://gizmodo.com/this-35000-computer-is-powered-by-trapped-human-brain-cells-2000573993](https://gizmodo.com/this-35000-computer-is-powered-by-trapped-human-brain-cells-2000573993) [^3]: Pandi, A., Koch, M., Voyvodic, P.L. _et al._ Metabolic perceptrons for neural computing in biological systems. _Nat Commun_ **10**, 3880 (2019). https://doi.org/10.1038/s41467-019-11889-0 [^4]: Brett J. Kagan et al., "In vitro neurons learn and exhibit sentience when embodied in a simulated game-world," _Neuron_, October 12, 2022. DOI: 10.1016/j.neuron.2022.09.001[4](https://www.sciencemediacentre.org/expert-reaction-to-study-in-which-human-and-mouse-neurons-in-a-dish-learned-to-play-the-video-game-pong/). [^5]: Smirnova, L., Caffo, B. S., Gracias, D. H., Huang, Q., Morales Pantoja, I. E., Tang, B., Zack, D. J., Berlinicke, C. A., Boyd, J. L., Harris, T. D., Johnson, E. C., Kagan, B. J., Kahn, J., Muotri, A. R., Paulhamus, B. L., Schwamborn, J. C., Plotkin, J., Szalay, A. S., Vogelstein, J. T., … Hartung, T. (2023). Organoid intelligence (OI) : The new frontier in biocomputing and intelligence-in-a-dish. _Frontiers in Science_, _1_, 1017235. [https://doi.org/10.3389/fsci.2023.1017235](https://doi.org/10.3389/fsci.2023.1017235)