Neurons on the Edge: Brain Avalanches Reveal the Secrets of Information Processing
summary: Researchers have provided strong evidence supporting the controversial “critical brain hypothesis” through a project called DishBrain.
This experiment, involving 800,000 human neurons playing a game of pong, reveals how neurons switch into a ‘neur-critical’ state when informed by the surrounding environment, enabling a cascade of brain activity. This state lies between the extremes of epileptic arousal and the comatose state.
The findings suggest profound insights into brain function and potential treatments for neurological disorders.
Key facts:
- DishBrain, a collection of 800,000 human neurons, was used to support the critical brain hypothesis by demonstrating how neurons switch into a “neurological critical” state.
- This ‘neurologically critical’ condition enables complex behaviors, present between maximal epileptic activity and signaling arrest.
- While criticality helps to understand neural responses, it alone does not lead to learning; A feedback loop with additional procedure outcome information is essential.
source: Cortical Labs
Paper published in Nature Communications It shows that when neurons are given information about the changing world around them (task-related sensory input), they change how they behave, putting them on edge so that even small inputs can then trigger “snowslides” of brain activity, supporting a theory known as In the name of the critical brain hypothesis.
Researchers from Cortical Labs and the University of Melbourne used DishBrain, a collection of 800,000 human neurons that learn to play ping pong.
It is the strongest evidence to date supporting the controversial theory of how the human brain processes information.
According to the critical brain hypothesis, large, complex behaviors become possible only when neurons are on edge such that small inputs can trigger “melts” of brain activity.
This delicately balanced state is known as the “neurological critical” state, and lies between two extremes – the unbridled excitement seen in disorders such as epilepsy, and the comatose state in which signals cease.
“Not only does this show reorganizing the network to a near-critical state where it is fed structured information, but getting to that state also leads to better task performance,” says Dr. Brett Kagan, chief scientific officer of biotechnology startup Cortical Labs. DishBrain.
“The results are amazing, far beyond what we thought we would achieve.”
The research adds a vital piece to the puzzle of the critical brain hypothesis.
To date, there has been little empirical evidence to show whether significance is a general feature of biological neural networks or whether it is related to information load.
“Our results indicate that near-critical network behavior emerges when a neural network is engaged in a task, but not when it is left unstimulated,” says Dr. Kagan.
However, Dr. Kagan’s research shows that significance alone is not sufficient to drive learning through a neural network.
“Learning requires a feedback loop, where the network is given additional information about the consequences of the action,” says Dr. Kagan.
The latest research confirms DishBrain’s ability to help unlock the secrets of the human brain and how it works, which is not possible with animal models.
“Usually to study the brain, especially at the level of neurons, researchers have to use animal models, but in doing so, there are a lot of difficulties and one can only have a limited number of subjects,” says first author Dr. Forough Habibullah. Research fellow at Cortical Labs.
“So when I saw DishBrain’s unique ability to answer different types of questions in a way no one else could, I was really excited to start this project and join the team.”
Doctors also see great potential for the research to help discover treatments for disabling brain diseases.
“The critical DishBrain project has been an amazing collaborative experiment between Cortical Labs, biomedical engineering, and neuroscience,” says paper author Dr Chris French, Neurodynamics Laboratory Leader at the University of Melbourne’s Department of Medicine.
“Critical dynamics of DishBrain neurons should provide key biomarkers for the diagnosis and treatment of a range of neurological diseases, from epilepsy to dementia,” he says.
By building a living model brain, scientists will be able to experiment with using real brain functions rather than similar, flawed computer models to not only explore brain function but also to test how drugs affect it.
Professor Anthony Burkett, author of the paper and Head of Biosignals and Biosystems at the University of Melbourne, says the research also has the potential to solve challenges for brain-computer interfaces that can restore functions lost as a result of neuronal damage. Department of Biomedical Engineering.
“A key feature of the next generation of neural prostheses and brain-computer interfaces that we are currently looking at involves the use of real-time closed-loop strategies,” he says. “So the results of this study could have important implications for understanding how these control and motivation strategies interact with neural circuits in the brain.”
“This field of biological modeling of the brain is still in its infancy, but it opens the way to a whole new area of science,” says Dr. Kagan.
About this neuroscience research news
author: Niall Byrne
source: Cortical Labs
communication: Niall Byrne – Cortical Labs
picture: Image credited to Neuroscience News
Original search: open access.
“Critical dynamics arise during the presentation of structured information within neural networks embodied in the laboratory” by Brett Kagan et al. Nature Communications
a summary
Critical dynamics arise during the presentation of structured information within neural networks embodied in the laboratory
Understanding how the brain processes information is a very difficult task. Among measures that characterize information processing in the brain, observations of semi-critical dynamic states have aroused great interest.
However, theoretical and experimental limitations associated with human and animal models have prevented a definitive answer as to when and why neurocriticality arises with links from attention, to cognition, and even to consciousness.
To explore this topic, we used a laboratory neural network of cortical neurons trained to play a simplified game of “Pong” to demonstrate artificial 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 is associated with proximity to critical dynamics. However, significance alone is insufficient for a neural network to demonstrate learning in the absence of additional information regarding the consequences of previous actions.
These findings provide convincing support that neuronal significance arises as a key feature of processing incoming structured information without the need for a higher level of cognition.
