Brain functions best in a balanced state, new study claims

DishBrain learned to play Pong

According to the scientists, these behavioral changes put them on edge, setting off tiny input ‘avalanches’ of brain activity, supporting a theory known as the critical brain hypothesis.

An experiment was conducted to understand the complexity of this brain activity where the researchers grew human brain cells outside the human body. They then trained the cells to play a video game called ‘Pong.’

As a result, the brain cells are organized in a particular way and act like a computer. The brain cells were called – ‘DishBrain’ and constituted 800,000 human neural cells that learned to play Pong.

Essentially, they were trying to see if these brain cells could perform tasks and learn from them, just like a computer or a person can learn how to play a game better over time. This experiment helps us understand more about how the brain works and how it can be used for different purposes.

The study explained: “To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’ to demonstrate Synthetic Biological Intelligence (SBI).”

In an official statement, scientists claimed that this research has shown the strongest evidence to date in support of a controversial theory of how the human brain processes information.

Best functioning in the "neural critical" state

Therefore, the brain works best in a balanced or organized state under the critical brain hypothesis. It’s not too overactive, such as in the case of epilepsy, or underactive, like in the case of a coma.

The balance permits even the smallest signals to trigger significant brain activity, which allows humans to perform complex tasks. This balanced circumstance is known as the “neural critical” state. 

Dr. Brett Kagan, chief scientific officer of the biotech start-up Cortical Labs and creator of DishBrain, stated: 

“It not only shows the network reorganizing into a near-critical state as it is fed structured information but that reaching that state also leads to better task performance. The results are astonishing, way beyond what we thought we would achieve.”

Dr. Kagan further explained that the results suggest the emergence of near-critical network behavior when the neural network is engaged in a task but not when left unstimulated.

Paving the way for further studies

Although Dr. Kagan’s research has also demonstrated that criticality alone is insufficient to drive learning by a neural network, the statement noted. This study is not possible with animal models, but it is helping to uncover the secrets of the human brain.

Dr. Chris French, leader of the Neural Dynamics Laboratory at the University of Melbourne’s Department of Medicine, stated:

“The critical dynamics of the DishBrain neurons should provide key biomarkers for diagnosis and treatment of a range of neurological diseases from epilepsy to dementia.”

The researchers plan to build a living brain model and experiment using real brain function to explore how the brain functions and test how drugs affect it, scientists shared.

Professor Anthony Burkitt, an author of the paper and chair of Bio Signals and Bio-Systems of the University of Melbourne’s Biomedical Engineering Department, highlighted the importance of the research.

He said that the research had the potential to solve challenges facing brain-computer interfaces that could restore functions lost as a result of neural damage.

“A key feature of the next generation of neural prostheses and brain-computer interfaces that we currently researching involves utilizing real-time closed-loop strategies,” he stated. “So the results of this study could have important implications for understanding how these control and stimulation strategies interact with the neural circuits in the brain.”

The study was published on August 30 in the journal Nature.

Study abstract:

Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’ to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.