Brain Cells on a Computer Chip Offer Advanced Medical Treatments and Use Less Energy

Cortical Labs, an Australia-based startup, has developed what it describes as a “code-deployable biological computer.” Called CL1, this technology is a type of synthetic biological intelligence consisting of a combination of real neural networks and computer chips.

Human neurons are grown on a silicon chip, creating a fusion of brain cells and silicon. The silicon chip sends and receives signals to and from neurons in a sort of feedback loop, similar to the way neurons send signals to each other. By integrating silicon and living tissue, computer code can be sent directly to neurons.

Cells grown on a computer chip

It looks a bit like brain-on-a-chip technology, and there are some similarities. In fact, CL1’s immediate predecessor was DishBrain, a network of brain cells on a plate. Brett Kagan, chief science officer and chief operating officer at Cortical Labs, led the team that designed DishBrain and taught it how to play the classic video game Pong.

DishBrain cell cultures learned to track the ball and control the racket in the popular tennis-style video game. The team published the results of their study on DishBrain in 2022 in the journal Neuron. CL1, Kagan says, “is the next evolution of brain-on-a-chip technology.”

The 800,000 neurons that make up CL1 are made from human skin cells and blood cells that have been converted back into stem cells and then reprogrammed to become brain cells. The cells are grown directly on a computer chip, with electrical contacts connecting the biological and the digital. Or as the company’s website puts it: “We start with what digital AI models spend enormous resources trying to imitate.” »

Cells are kept alive by a life support system that filters waste, provides nutrients, and regulates gases, acidity, and temperature.

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Not so traditional training

The methods used to train CL1 are also innovative. Traditional machine learning was designed for silicon computing systems, Kagan explains. The methods used here “draw inspiration from neuroinformatics theories of how the brain works,” he says. These theories include the free energy principle and Karl Friston’s active inference frameworks.

Nabil Imam, a researcher at Georgia Tech University who works on biological computing, describes this as a “niche technique” in machine learning, but adds that nothing about this technique requires it to be in a dish.

“Everything they did in the dish can actually be done on a regular computer using neural networks,” he says.

What are the advantages of CL1?

However, CL1 has some distinct advantages. The system requires a fraction of the energy used by conventional AI data centers, an important factor as global warming rapidly worsens. Additionally, it could potentially reduce the need for animal models in certain types of research.

Other benefits of CL1, Kagan adds, are the ability to learn with very limited data, the ability to generalize, and the ability to deal with fuzzy data and changing dynamic environments in real time. After all, as the company’s website states, “the neuron is self-programmed, infinitely flexible and is the result of four billion years of evolution.”

Currently, the most likely uses for CL1 are drug development, personalized medicine, and neuroscience research, although Kagan says people are exploring CL1 as an alternative to traditional AI and robotics technologies.

“The goal of this technology is not to replace current computing methods, but to provide a better tool where current methods fail or require huge amounts of data or energy to train,” he adds.

Is this a better approach than simple silicon? Not necessarily, according to the Imam. However, AI is still in its infancy, he says.

“We don’t know the best way to solve many of these problems, and we know that the brain solves a lot of things very efficiently. It’s the most efficient intelligence system there is.” And now it has a silicon partner.

Learn more: How scientists are building a better brain-on-a-chip

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