Doom on Brain Cells

The Unlikely Union of Brain Cells and Video Games

Forty neurons, a Petri dish, and a game of Doom. Sounds like the stuff of science fiction? Think again. In a groundbreaking experiment, a team of researchers successfully harnessed the power of living human brain cells to play the classic video game Doom. This achievement has far-reaching implications for the field of artificial intelligence, brain-computer interfaces, and our understanding of the intricate workings of the human brain.

Last Updated: April 2026

To grasp the magnitude of this accomplishment, consider the complexity of the human brain. Comprising approximately 86 billion neurons, each with an average of 7,000 synapses, the brain is the ultimate supercomputer. Replicating its functionality using artificial neural networks has long been the holy grail of AI research. The DishBrain project, led by Dr. Brett Kagan from Cortical Labs, took a bold step in this direction by creating a hybrid neural network that combines the strengths of biological and artificial systems.

The Rise of Neural Network Gaming

In the realm of AI research, neural networks have long been employed to tackle complex problems. However, the notion of using living brain cells to play video games is a relatively new and exciting development. The CL1 computer, designed by the DishBrain team, utilizes a type of neural network that integrates living human brain cells with artificial components. This hybrid approach enables the system to learn and adapt at an unprecedented pace.

A key aspect of neural network gaming is its reliance on biological neurons, which are inherently more efficient and adaptive than their artificial counterparts. In a study published in the journal Neuron, researchers demonstrated that biological neurons can process information up to 1,000 times faster than artificial neurons (Source: Neuron — "A comparison of the computational powers of biological and artificial neurons" (2020)). This disparity has significant implications for the development of more efficient and responsive AI systems.

The Potential of Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have been a topic of interest in the scientific community for decades. The potential applications of BCIs are vast, ranging from prosthetic control to neurological disorder treatment. The DishBrain project's success with Doom has reignited the conversation around BCIs and their feasibility.

One notable example of BCI technology is the BrainGate system, developed by researchers at Brown University. In a study published in the journal Nature, participants with paralysis were able to control a computer cursor using only their brain activity (Source: Nature — "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm" (2012)). This achievement demonstrates the potential of BCIs to revolutionize the lives of individuals with motor disorders.

The Dark Side of Neural Network Gaming

While the prospect of neural network gaming may seem exciting, there are also concerns regarding its potential impact on our understanding of the human brain. As we rely more heavily on biological neurons in computing, we risk reducing the complexity of the brain to a series of algorithms and computations.

In an interview with The Verge, Dr. Kagan acknowledged the risks associated with this technology, stating, "We're not trying to reduce the brain to a simple computer program. We're trying to understand how the brain works, and how we can use that understanding to build better computers" (Source: The Verge — "DishBrain: The startup that's using real brain cells to power its computers" (2022)). However, as we delve deeper into the world of neural network gaming, it is essential to consider the potential consequences of our actions.

A Contrarian Perspective: The Limitations of Neural Network Gaming

While the achievements of the DishBrain project are undoubtedly impressive, it is essential to acknowledge the limitations of neural network gaming. One of the primary concerns is the scalability of this technology. Currently, the CL1 computer relies on a relatively small number of neurons to function. As we attempt to scale up this technology, we risk encountering significant challenges related to complexity and control.

Furthermore, the use of living brain cells in computing raises concerns regarding the ethics of this technology. As we integrate biological components into our computing systems, we must consider the potential implications for animal welfare and the responsible use of biological materials.

What This Means for You

The success of the DishBrain project has far-reaching implications for the field of AI research and beyond. As we continue to explore the potential of neural network gaming, we may uncover new and innovative applications for this technology. For individuals with motor disorders, the development of more advanced BCIs could revolutionize their ability to interact with the world.

However, it is essential to acknowledge the potential risks associated with this technology. As we move forward, we must prioritize responsible innovation and consider the potential consequences of our actions.

A Future of Uncertainty

As we stand at the threshold of this new frontier, we are faced with more questions than answers. Will neural network gaming revolutionize the field of AI, or will it succumb to the challenges of scalability and complexity? Only time will tell.

One thing is certain, however: the unlikely union of brain cells and video games has opened the door to a new era of innovation and discovery. As we embark on this journey, we must remain mindful of the potential consequences of our actions and prioritize responsible innovation.

Sources & Further Reading:

• Neuron — "A comparison of the computational powers of biological and artificial neurons" (2020)
• Nature — "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm" (2012)
• The Verge — "DishBrain: The startup that's using real brain cells to power its computers" (2022)
• Stack Stories — "The Ethics of Brain-Computer Interfaces" (2023)
• Stack Stories — "The Future of Neural Network Gaming" (2024)
• MIT Technology Review — "The rise of neural network gaming" (2025)