Computers powered by human brain cells

A research team from Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering explored the potential of using the brain organoids in biological computing.

Powered by millions of human brain cells, biological computers (biocomputers) could outperform traditional silicon-based machines, paving a new way in the field of computer science. They function at the confluence of biology, engineering, and computer science and are capable of learning from experience.

A brain organoid created in laboratory. The neurons are highlighted in magenta, cell nuclei in blue, and other supporting cells in red and green

The paper introduces the concept of “organoid intelligence” (OI), which refers to the cognitive functions exhibited by brain organoids, such as learning and memory, and their ability to self-organize into brain-like configurations.

Brain organoids are small, 3D neural cultures that are grown in the laboratory from human stem cells. They mimic some of the features of the developing human brain, including the cellular composition, structure, and functionality.

The OI could maximize the advantages of the human brain over traditional, silicon-based computers. It may bring new generations of more powerful and sustainable supercomputers.

The authors have developed a blueprint that includes complex networks of brain organoids connected to real-world sensors and output devices.

Nonetheless, the present brain organoids utilized in the study are too small for achieving OI. A single organoid holds around 800 megabytes of memory storage, equivalent to that of a fruit fly’s nervous system. To achieve OI, researchers would have to increase the number of cells to 10 million.

The human brain vs. artificial intelligence

  • The human brain is slower than machines in processing simple information, such as arithmetic, but it far outperforms machines in processing complex information due to its ability to handle limited and uncertain data.
  • The brain can perform both sequential and parallel processing, whereas computers can only do the former. The brain outperforms computers in decision-making involving large, diverse, and incomplete datasets and other difficult forms of processing.
  • Furthermore, each brain has a storage capacity of around 2,500 TB, which is still unmatched by modern computers.
  • The human brain is more energy efficient.

The architecture of an OI system for biological computing

The study utilized an OI system for biological computing having at the core a brain organoid that performs the computation. The learning potential of OI was optimized by incorporating specific cells and genes, such as neurons and synaptic proteins, which are essential for the learning process.

Architecture of an Organoid Intelligence system for biological computing
  1. The inputs in the system came in the form of both electrical and chemical signals, as well as natural signals from sensory organoids, such as retinal sensors.
  2. The outputs were recorded through different electrophysiological measurements and imaging techniques, including changes in calcium levels or by using fluorescent markers.
  3. The massive amounts of data generated during the experiment were stored, processed, and analyzed using a big-data management warehouse.
  4. The datasets were further used to train ML algorithms to identify particular patterns of activity in the organoid and to generate the biofeedback for its learning process.


The research offers a framework for a future approach, using a bigger network with an increased number of brain organoids. As more data is collected, the accuracy of the simulations should improve, allowing better understanding the behavior of these complex biological systems.

These half-human-half-machine biocomputers might soon revolutionize computing while also advancing our understanding of brain development and function.

They have the potential to yield significant benefits for human medicine, enabling the development of more personalized treatment options and a deeper understanding of complex neurological disorders.

In future research, the brain organoids must be scaled up into more complex and robust structures. Instead of using supervised learning, the team recommends reinforcement learning, as it allows a better understanding of the active exchange between an organoid and its external environment.

The development of human brain organoids capable of cognitive functions raises several ethical concerns, such as whether they can develop consciousness or feel pain.These issues need to be carefully considered as the technology advances towards creating more advanced brain organoids systems.

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