Among human body organs, the brain has been viewed as the most complex: an enormously intricate network of neurons working together to maintain everything from memory to emotion and, of course, decision-making. Traditionally, neurons have been perceived as key players in such a great orchestra, whereby their firing, wiring, and synaptic connections have traditionally dominated the concerns of neuroscience. But what if the neurons are actually more like the stadium that contains the real action—the neural signals—to take place? Let us assume this. How then would it change our understanding of the brain, learning, and even artificial intelligence?.
The neurons, the very building blocks of the brain, have practically become legendary for their feats in processing and transmitting information. We talk about them firing at will in response to stimuli, wiring together to form complex networks, and organizing in hierarchies to govern our cognitive functions. Realistically, however, neurons are probably less like agents who do things and more akin to venues where the activity performed by neural signals is the real action.
Those electrical and chemical signals are what really bring about change in the brain. They transmit information, dictate neuronal firing, and eventually determine thoughts, memories, and emotions. Neurons give the framework, but it is the signals which do all the hard work—determining what is stored as a memory, what kind of emotion is felt, or what decision is made.
Neural signals work together in sets, almost like “loops” or “sets.” These loops are how the brain keeps information—memory, emotion, and sensory input—organized. Think of this: every memory or emotion is just a different loop made of signals that hold a particular pattern as they move around the network of neurons.
It explains how it is possible that this organization allows for such efficiency in storing and accessing different memories, emotions, and thoughts. It’s not about the localization of these memories within the brain, but more about how these sets of signals are configured and how they interact with one another. It is the tiny differences in signal loops that make one memory different from another or a memory from an emotion. These loops further determine the intensity of attention, degree of awareness, and even free will.
In a new study published in Nature, researchers detail how these loops might support learning and behavior. The team showed that neurons in the hippocampus—one of the brain’s key areas for memory—can encode several variables in a disentangled, abstract format. The ability to form these abstract representations lets the brain generalize and apply learned information to new situations—a hallmark of adaptive behavior.
The question at this point would be: How are these abstract neural representations, or complex geometries of signals, communicated across parts of the brain? Given that neurons are relatively immobile, how is it possible for them to express such a large amount of dynamic and complex information?
If learning is indeed based on these neural representations, then the process of relaying them must involve something more dynamic than just the neurons themselves. This is where neural signals come in. It’s theorized that electrical signals carry summaries of the complex configurations of chemical signals across the brain. These summaries may not be detailed blueprints but rather condensed versions that still retain the essential information needed for processing and learning.
It is in the greater potential for variation and complexity that chemical signals have over being solid that may permit a greater range of variation and complexity. The result of this kind of fluidity, along with the direct relationship between electrical and chemical signals, may explain how the brain is able to handle such a large variety of cognitive functions with such ease and flexibility.
ANNs are designed basically on some simplified idea of how neurons work, that is, their firing, wiring, activation, and inhibition. These are digimodels of the brain, though still very different from the real thing, especially with regard to the importance of signaling. ANNs are more like blueprints of a stadium, players, and audience, because that is what makes the game come alive.
For example, large language models have been able to come very close to the ability of humans in processing the language, thereby suggesting that they work or perform tasks within their design but lack dynamic and varied signals animating in the human brain. They work very well within the limits of their design, but they simply do not truly reproduce the way our brains work. The understanding of how signals drive brain computation may lead to advances in neuroscience and AI.
If we think in terms of intelligence, human, animal, or artificial, it may be more accurate to consider it in terms of how memory and knowledge are put to use efficiently. One may look at intelligence as a factor of maximizing the use of memory by the effective relay of signals. In human beings, this is very, very highly developed; that is why it can sustain thought, emotion, and behavior of a very, very elaborate nature. It is essentially the same thing with animals, only perhaps less developed.
Artificial systems, such as LLMs, are very good at packing their available memory into the execution of specific tasks, hence producing outputs which many a time rival those of human experts. To realize the next level of artificial intelligence, we arguably have to go beyond simple neural models and start adding the kind of complicated dynamics of signal processing that we see in biological systems.
Understanding the brain isn’t just about mapping out its structure or decoding the functions of neurons. Rather, it’s about the dynamic and intricate dance of signals that orchestrate everything done by the brain. Such a shift in focus from neurons to signals could unlock new ways to understand learning, memory, and intelligence—both in humans and in machines.
The future of AI will very much depend on how close we can approach models for these signal processes to bring us one step closer toward the creation of systems that not only can imitate but understand and interact with the world as dynamically as the human brain does.
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Chloe Walker
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Samuel Davis is a Data Scientist passionate about solving complex problems and turning data into actionable insights. With a strong foundation in statistics and machine learning, Samuel enjoys tackling challenges that require analytical rigor and creativity.
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