A groundbreaking wave of neuroscience research is redefining what it means to think—and, crucially, why artificial intelligence (AI) still falls far short of the intricacies of the human mind. A newly published feature in Salon highlights the distinct evolutionary adaptations that make the human brain more than a glorified computer, challenging decades-old assumptions fundamental to AI development and the neural network concept that underlies machine learning models.
For years, popular understanding—and much of AI research—has treated the brain as a vast network made up of nearly identical neurons whose intelligence emerges through the patterns of their collective firing. This view inspired so-called artificial neural networks, computer systems designed to solve problems by mimicking the way brains process information. Such analogies, cemented over decades, fostered the belief that if machines could imitate the structure and connectivity of brains, they might one day match, or even surpass, human intellect. But recent scientific discoveries show this metaphor misses the mark in fundamental ways, with profound implications for both neuroscience and the future of AI.
Why does this matter for Thai readers? As Thailand accelerates its adoption of digital transformation policies and invests heavily in AI development for public health, education, and business, understanding the true nature of intelligence—both artificial and biological—is central to shaping effective technology, education, and research policies. The new findings also deepen our comprehension of the uneven progress of AI in Thai education and healthcare, as well as ethical debates about relying on AI in these domains.
Over the last two decades, neuroscientific evidence has steadily eroded the simple “neural network” analogy. Advanced research into human and animal brains—particularly at the single-neuron level—reveals that not all neurons are alike. While traditional AI models assume all of their digital “nodes” function interchangeably (just as the older view held about biological neurons), the human brain consists of a dazzling variety of neurons, each with specialized roles honed by millions of years of evolution. These unique neuron types, according to UCLA neurosurgeon and researcher, encode specific concepts, memories, and sensory information in ways that current AI architectures simply cannot replicate. A recent study classified at least two million human neurons into 31 superclusters, 461 clusters, and 3,313 subclusters—a level of complexity dwarfed by the relatively simplistic architectures of today’s AI.
One of the most compelling discoveries involves so-called “concept cells”—sometimes popularly called “grandmother cells”—which can fire in response to highly specific, abstract ideas or memories. In famous experiments conducted with epilepsy patients undergoing necessary brain surgery, researchers recorded single neurons that fired only when the subject thought about, saw, or remembered a singular concept like “The Simpsons.” In a striking case, a specialized neuron activated not only while a person watched a clip from the cartoon but also when they merely remembered the experience. This suggests that a single neuron can encode and trigger the recall of a complex concept, contributing to the rapid, abstract, and flexible thinking that characterizes human cognition. In contrast, machine learning models require massive numbers of examples to form only the vaguest statistical associations—often missing subtle or contextual cues entirely.
According to cognitive scientists at Stanford University, present-day AI models exhibit crucial deficiencies compared to the human brain’s innate and evolved capabilities. Not only do artificial neural networks require enormous datasets to “learn” a task, they also lack the biological predispositions and specialization that allow human brains to process information rapidly and with astonishing accuracy, even from minimal input. This gulf is not merely a technical problem but a product of millions of years of evolutionary adaptation—much of it unique to humans.
Crucially, researchers believe that the underlying learning mechanisms in brains and AI differ fundamentally. The dominant “Hebbian” learning model in AI, named after early theory positing that neurons that fire together strengthen their connections (“cells that fire together wire together”), powers deep learning but demands relentless exposure to data. In human brains, however, newer research points to alternative models of “synaptic plasticity,” like Behavioral Time Scale Synaptic Plasticity (BTSP), which allows for the creation of memories with far fewer repetitions and “events.” Such “sparse coding” means that just a few neurons—sometimes even just one—can reliably represent a critical concept, allowing for more energy-efficient, flexible, and creative thinking.
These findings also have Thai cultural and practical significance. With the rapid adoption of AI-powered platforms in language translation, telemedicine, and personalized education—fields directly affecting the Thai public—there is often an implicit expectation that machines can perform at, or near, human levels. However, the mismatches between AI’s pattern-based learning and the meaning-laden, context-rich way humans understand culture, language, and emotion mean that automated systems are still limited in fields requiring human-level interpretation or sensitive care. Thai educators and healthcare professionals, increasingly asked to integrate AI into classrooms and clinics, benefit from understanding these boundaries to temper expectations and design more effective human-AI collaboration.
Historically, advances in both neuroscience and technology have been tightly coupled; the brain-as-computer analogy underpinning much of the past century’s thinking drove not only AI research globally but also shaped public imagination in Thailand, where technology-focused educational reform and coding for youth have been emphasized by recent Ministry of Education initiatives. Yet, as neuroscience repeatedly finds specific “building blocks” for abstract thought unique to humans—enabling functions like language, creativity, empathy, and cultural memory—AI’s limitations become both clearer and more understandable. For instance, automated essay graders, tutoring bots, and health diagnostics currently in trial across Thai schools and clinics often struggle with nuances, cultural references, and contextual understanding that come naturally to human professionals (source: World Bank, UNESCO reports on AI in Thai education; Thai Ministry of Health pilot projects).
Expert perspectives add depth to this moment of scientific re-evaluation. A cognitive and clinical neurophysiologist from the University of Bonn, whose pioneering work with single-neuron recordings has shaped the field, explains: “When I say sparse versus network, or sparse versus distributed, that means that most neurons are silent, and then just a few neurons suddenly say ‘Look, this is my favorite stimulus.’ It indicates that that stimulus is there. They actually provide the semantic building blocks that are being pieced together to form mnemonic episodes.” Such insights clarify why even the world’s most advanced AIs, powered by ever-expanding global datasets and hardware, still cannot spontaneously “understand” in the sense humans do, nor encode memories and experiences as efficiently.
Looking ahead, these revelations may signal a turning point for how Thailand and the broader world approach AI. By redirecting some research energy toward understanding and mimicking the diversity and specialization of actual brain cells—and not just the architecture of networks—technology developers may find new ways to improve machine “understanding,” particularly in areas requiring context, empathy, and cultural sensitivity. At the same time, these studies remind policymakers and the Thai public to critically assess how and where to deploy AI, and where human judgment remains not only valuable but irreplaceable.
For everyday Thais, the practical takeaway is to see AI as a powerful tool, but one with real and present limitations, especially in tasks involving ambiguity, abstract thinking, or deep cultural context. While AI will continue to revolutionize aspects of healthcare, education, translation, and customer service—it will not soon replace the creativity, adaptability, and wisdom honed by millions of years of human evolution. Thai students, workers, and policymakers can maximize benefits by combining the speed and scale of AI with the empathy and nuance of human intelligence.
Readers curious about these developments should follow leading neuroscience and AI research to stay informed, critically assess tech claims, and push for inclusive policies that balance innovation with human values. As Thailand charts its path into the digital future, understanding what truly sets the human brain apart will help ensure AI complements rather than competes with our most human qualities.
Sources:
- Salon Magazine, “Humans think — AI, not so much. Science explains why our brains aren’t just fancy computers” (https://www.salon.com/2025/04/25/humans-think--ai-not-so-much-science-explains-why-our-brains-arent-just-fancy-computers/)
- Cell journal (https://www.cell.com/)
- World Bank, UNESCO reports on AI in Thai education
- Thai Ministry of Health policy briefs
- Merriam-Webster Dictionary, definition of “neural network” (https://www.merriam-webster.com/dictionary/neural%20network)