Wetware Computing Computers Now Run With Real Brain Cells

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In biocomputers, neurons derived from stem cells merge with specialized silicon chips. This results in sensational advantages in terms of energy efficiency, adaptive learning, and natural adaptability.

With the CL1, the Australian biotech startup Cortical Labs has introduced the world's first biocomputer. This is a biological computing system, also known as wetware computing or Dishbrain, that combines living human neurons with silicon hardware to create dynamic neural networks.

In contrast to conventional processors, which rely on fixed electronic circuits, so-called Synthetic Biological Intelligence, or SBI, enables a much more effective, flexible, and adaptive computing framework.

Development of the Neurons

For this purpose, a small amount of blood must first be taken from volunteers, enabling the reprogramming of cells into stem cells and their subsequent differentiation into neurons. These neurons are then cultured in a nutrient-rich solution and grown on a silicon chip with a 59-electrode array.

Such a method allows Cortical Labs to produce lab-grown neurons and integrate them into the biological computing system CL1. Together, they form a neural network that enables complex communication between biological and digital systems, thereby processing information and learning more effectively. CL1 is a high-performance closed system where real neurons interact with software in real time.

Minimal Viable Brain (MVB)

A research initiative by Cortical Labs aims to develop a controlled neural system capable of processing complex information with "minimal redundant cell differentiation." This means that while cells may change, these changes are not always necessary or beneficial for the body. These cells, in their new form, are thus considered "redundant."

The goal of this project is therefore the identification of the key biological components for dynamic and responsive information processing. This could provide a new platform for exploring advanced cognitive behaviors.

Ethical Questions

The MVB concept also presents researchers with the challenge of deeply engaging with the question of what constitutes consciousness in hybrid biological-digital systems. This sparks discussions about whether synthetic neural networks could ultimately develop forms of consciousness or sentience.

The biotechnology startup Cortical Labs is working on refining the MVB by researching the smallest functional neural network that could exhibit genuine cognitive behavior or perhaps even consciousness. This includes experiments with human-induced pluripotent stem cells (hiPSCs), which are integrated into high-density multi-electrode arrays (HD-MEAs) to create autonomous information exchange pathways.

DishBrain Technology

As early as 2022, Cortical Labs utilized neurons for an early form of this technology. This refers to a system designed to create a hybrid system capable of solving tasks and "learning" from these processes. Neuronal cells, mostly from embryonic rats, were cultured in an artificial environment and connected to an electronic circuit. This connection enabled the exchange of electrical signals between the cells and their surroundings.

The system was developed by Cortical Labs in collaboration with scientists from Monash University. An example of this is that DishBrain was already capable of playing the classic computer game Pong in 2022. For this, the cells were supplied with electrical stimuli to inform them of the ball and paddle positions. Through a system of reward and punishment—predictable or unpredictable stimulations —the cells learned to hit the ball.

Digitization of Biology

CL1 uses a proprietary Biological Intelligence Operating System (biOS) and offers a Python API for enhanced programmability and customization. The neurons reside in a nutrient-rich solution and are maintained in an environment that mimics human physiological conditions, where the neurons form connections and respond to electrical stimuli.

This controlled life-support unit is capable of maintaining the cellular health of the neurons for up to six months through pumps, gas, and temperature regulation. After this period, they typically need to be replaced, as they die due to the natural limitations of the life-support system. However, some experiments have achieved survival rates beyond six months, with some cells surviving up to a year.

Applications for Biocomputers

The CL1 biocomputer was developed, among other things, with the goal of realizing adaptive and more efficient robotic intelligence. In this way, robots could learn and respond essentially like humans. The CL1 enhances robotic intelligence with biological learning capabilities and enables human-like adaptive responses.

The technology could significantly advance robotics, particularly in applications requiring dynamic adaptation and complex decision-making in unknown environments. The system's flexibility, including a programmable bidirectional interface and a Python API, allows researchers to design customized experiments for robotics applications. Furthermore, the CL1 holds potential in drug discovery, disease modeling, and the study of brain functions, cognitive processes, and the effects of drugs on human brain cells. In addition, the system offers an ethically acceptable alternative to animal testing by providing more relevant human data, leading to more accurate insights into neurological diseases such as Alzheimer's and Parkinson's.

In addition to a physical product, Cortical Labs also offers a service known as Wetware-as-a-Service (WaaS). Using the Cortical Cloud, it is possible to purchase storage time on the chips. This allows interested customers to remotely access cultivated cells and work with them to create applications.

Advantages And Disadvantages At A Glance

Biological computers can offer advantages over conventional AI models:

• The adaptability of neurons leads to improvements in robotics, automation, and complex data analysis.
• Biological neurons can learn more organically and naturally and adapt better. This is especially true for areas such as pattern recognition and decision-making in unpredictable environments.
• Each unit or an entire rack of the CL1 consumes only about 850 to 1,000 watts of energy, which represents exceptionally high energy efficiency given the complex biological processes.

The disadvantages include:

• The scalability of this technology is currently considered uncertain. The production and maintenance of neuron-based systems are significantly more complex than the manufacturing of traditional processors.
• Ensuring long-term stability comes with additional challenges.
• Ethical concerns also arise from the use of human brain cells in technology. While the neurons used in the CL1 are lab-grown and, according to the manufacturer, devoid of consciousness, further advancements in this field may require guidelines to address moral and regulatory issues.

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