Single Brain Cells May Be the Hidden Secret Behind Human Intelligence

Rethinking What Makes Us Smart

For decades, scientists believed the secret to human intelligence was simple math: more brain cells, more connections, more power. The bigger the network, the smarter the animal. But a landmark new study published this week in the Proceedings of the National Academy of Sciences turns that assumption on its head — and the answer may be hiding inside a single cell.

For many years, the prevailing view was that the secret of human intelligence lay mainly in scale: the sheer number of neurons in the human brain — close to 100 billion — and the vast network of connections among them. But the new study suggests that part of the answer may lie at a much smaller scale: in the extraordinary computational power of individual brain cells.

One Cell, Remarkable Capability

The study, led by researchers at the Hebrew University of Jerusalem, revealed that human cortical neurons possess a massive computational advantage over other mammals, driven by their intensely dense, richly branching dendritic trees and highly specialized, unique electrical properties. Dendrites are the treelike extensions that branch off neurons to receive signals — and in humans, they appear to be doing far more heavy lifting than anyone realized.

Rather than needing a massive web of thousands of cells to execute basic discrimination tasks, the rich branching structure allows a single human cortical neuron to perform advanced computations independently — such as processing visual inputs to distinguish between completely different complex images, like a cat versus a dog. That kind of task was previously thought to require the coordinated effort of many neurons working together.

This means that a single human cortical neuron is not just a simple "on-off" building block in the brain; it is already a sophisticated computing unit in its own right, with computational capabilities equivalent to those of a deep neural network.

How Researchers Measured It

The team introduced a deep-learning-based measure called the Functional Complexity Index (FCI), which quantifies the input-output complexity of individual cells. Applying it to detailed models of human and rat cortical pyramidal neurons, they uncovered a pronounced species gap: human neurons exhibit significantly higher complexity.

Mechanistically, this advantage is attributable to expanded dendritic surface and richer branching, together with greater density and nonlinearity of NMDA-receptor signaling. NMDA receptors play a critical role in how neurons process and strengthen signals — and in humans, they appear to amplify each cell's independent computing ability in ways not seen in other mammals.

Interestingly, the layerwise profiles also diverge between species, with neuron complexity peaking in layer 2/3 in humans, but in layer 5 in rats — suggesting different allocations of computation across the cortex. This hints that human brains don't just have more powerful cells, but organize that power differently.

What This Means for AI and Neuroscience

Researchers found that neurons in the human cortex are significantly more complex information-processing units than those of other mammals — a finding that reshapes our understanding of where human cognitive abilities like language, imagination, and abstract reasoning actually come from. The implications stretch well beyond biology.

Today's state-of-the-art machine learning models are built from highly simplified, uniform mathematical points. This study offers a general framework linking physical cell geometry to raw processing power, providing a blueprint for a revolutionary new generation of brain-inspired AI. Professor Idan Segev notes that by replacing these basic, flat processing points with artificial nodes that mimic the deep, multi-layered computational power of a biological human neuron, scientists could build exceptionally powerful, compact, and energy-efficient AI networks.

The study reframes one of science's oldest questions — what makes us uniquely human — and finds the answer not in the vastness of our brains, but in the quiet, extraordinary sophistication of their smallest building blocks. If a single neuron can think in ways we never imagined, the full picture of human cognition may be far richer, and far stranger, than we've ever understood.