Researchers Gain Major Insights Into How Our Brains Work
Researchers from the Cognitive Computational Neuroscience Group at FAU have highlighted the brain’s predictive coding ability, which is essential for adaptive behavior. Using AI and data from epilepsy patients, they found that spontaneous brain activities play a crucial role in how the brain processes information without external stimuli. These findings may improve diagnostic and treatment methods for brain diseases and inspire AI technologies that mimic brain functions.
Predictive Coding and Brain Function
What comes next in a sentence? What will I see next? How does the environment change when I do this and what happens to my body when I do that? The human brain is continuously occupied at all levels of complexity and abstraction with predicting what will happen next. Known as predictive coding, this is considered one of the main tasks of the human super organ, making adaptive behavior possible and allowing us to find our bearings in our surroundings.
Dr. Patrick Krauss and Dr. Achim Schilling from the Cognitive Computational Neuroscience Group at the Chair of Computer Science 5 Pattern Recognition at FAU have succeeded in underlining this widely held hypothesis and contributing new findings in their recent study.
Innovative Study in Neuroscience
The two physicists and neuroscientists analyzed the spontaneous activity of the human brain using auto-encoders, an advanced form of artificial intelligence that allows patterns and connections to be perceived in the complex quantities of data provided by our brain that would have been unachievable using more traditional methods. This was made possible thanks to their collaboration with researchers from the Epilepsy Center at Uniklinikum Erlangen (speaker: Prof. Dr. med. Hajo Hamer). Epilepsy patients in the Center receive electrodes implanted into their brains before the surgical removal of epileptogenic foci.
Using the particularly rare and therefore especially valuable data received as a result, the researchers made a discovery that led to groundbreaking results: Certain spontaneous activities in our brain known as local field potential events (LFPs) were able to give decisive indicators regarding how our brains work. These spontaneous signals seem to play an important role in how our brains process information even in the absence of external stimuli.
Insights From Spontaneous Brain Activity
“In our study, we realized that our brains are constantly progressing through active states defined by these LFPs. It is as if our brains are constantly playing through various options for what might happen next even if we are not doing or perceiving anything in particular and not receiving any external stimuli at that moment in time,” stresses Dr. Patrick Krauss.
“We have also discovered that the form of these LFPs can determine the direction of information flux within the brain. This could give us important insights into how thoughts and feelings are processed in our minds,” adds Dr. Achim Schilling.
Advancing Brain Research Through AI
Findings that not only open new avenues for research but may also lead to better methods for diagnosis and treatment for brain disease. These AI-based methods can also be used in conjunction with normal EEG or MEG measurements, where electrodes are attached to the surface of the skull to measure brain activity.
“Knowledge of what our brains usually do while we are at rest can be put to good use for diagnostic purposes. If we can gain an ever better understanding of how our brains work and process information, that will allow us to develop more specific methods of diagnosis and treatment for neurological diseases,” emphasizes Dr. Achim Schilling. “If, for example, the brain enters a state that does not correlate with the external stimuli, that may be an indication of pathological changes.”
AI and Neuroscience: A Synergistic Approach
While AI is being used as a tool, the results of the study from the two FAU researchers may also help to further develop AI. The long-term aim: AI inspired by neuroscience that is capable of continuously making predictions, even if it is not currently processing any input. “This may be particularly useful in AI systems incorporated into vehicles, for example, especially when bearing safety in mind,” explains Dr. Achim Schilling.
Dr. Patrick Krauss continues, “Even if there is not much traffic and the car is only driving straight ahead on the highway, it would be beneficial for the AI to be considering in the background which traffic incidents could occur to which it may potentially have to react.”
The study from Dr. Patrick Krauss and Dr. Achim Schilling therefore shows that the synergetic connection between AI and brain research is capable of expanding the boundaries of what is known about cognitive processes and brain function, eventually leading to innovative new approaches in medical diagnosis and therapy.
The increasing fusion of technology and brain research also indicates how decisive interdisciplinary approaches are for decoding the complex systems found in nature. With their discoveries, the FAU researchers are approaching nothing less than a better understanding of the perhaps most complex of all systems: the human brain.
Reference: “Deep learning based decoding of single local field potential events” by Achim Schilling, Richard Gerum, Claudia Boehm, Jwan Rasheed, Claus Metzner, Andreas Maier, Caroline Reindl, Hajo Hamer and Patrick Krauss, 21 June 2024, NeuroImage.
DOI: 10.1016/j.neuroimage.2024.120696
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2 Comments
It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with only primary consciousness will probably have to come first.
What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.
I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.
My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461, and here is a video of Jeff Krichmar talking about some of the Darwin automata, https://www.youtube.com/watch?v=J7Uh9phc1Ow
“and inspire AI technologies that mimic brain functions.”
I’d rather not, thanks. Having human psychopaths running the world is bad enough.