Human thought is made of electrical signals. Before the binary codes of 0 and 1, the language of life was analog. The faint voltage spikes fired by neurons — brief bursts of energy known as 'spikes' — create our consciousness and memory. And now, science has begun to redraw that biological current onto silicon. In 2025, the development of 'biomimetic artificial neurons' is blurring the boundary between human brains and machines, opening a new frontier of intelligence.

The human brain is slow. A single neuron fires only a few hundred times per second. Yet its slowness is compensated by parallelism and connectivity. One hundred billion neurons, connected through one hundred trillion synapses, turn a single impulse into emotion and memory. Computers, by contrast, perform billions of operations per second — yet they do not think.

To bridge this gap, scientists have turned to 'neuromorphic technology'. Neuromorphic systems mimic the structure and function of the brain to fundamentally transform how information is processed. A recent study published in 'Nature' successfully replicated the core mechanism of biological neurons — 'ion flow and spike-based signaling' — using artificial devices such as 'memristors' and other resistive switching elements.

These artificial neurons are not simple transistors that carry current. They dynamically adjust their electrical threshold in response to external stimuli, and once that threshold is exceeded, they 'fire' an electrical signal — precisely as biological neurons do. In essence, the circuit “feels and responds” to input, rather than merely conducting it.

This new generation of artificial neurons possesses something traditional semiconductor circuits never could: 'self-adaptation'. The device alters its synaptic weight through the movement of ions, and after repeated exposure to the same input pattern, it begins to 'remember' that pattern. In other words, electrons are now learning from experience.

The research team connected hundreds of these devices into a simple neural network and successfully trained it to recognize both image patterns and sound signals simultaneously. Remarkably, the circuit consumed 'extremely low energy'. Just as the human brain operates on roughly 20 watts of power, this artificial network ran thousands of times more efficiently than a conventional GPU.

As a result, the architecture of AI is shifting — from 'code' to 'signal', from 'command' to 'response'. Machines are no longer merely calculating; they are beginning to 'feel' and 'adapt' through the flow of electrons.

The emergence of artificial neurons also brings the dream of 'Brain–Computer Interfaces (BCIs)' much closer to reality. Earlier BCIs could only read electrical signals from the brain and translate them into commands. Now, machines are beginning to understand the 'patterns and meanings' within those signals.

Because these artificial neurons exhibit electrical potentials and frequency behaviors similar to biological neurons, they can communicate directly with the nervous system. In one experiment, researchers connected an artificial neuron to the auditory nerve of a mouse, converting external sound into electrical impulses transmitted through the artificial circuit. Astonishingly, the mouse responded normally to the sound stimuli carried by the synthetic pathway.

This is more than prosthetics. It marks the dawn of 'hybrid intelligence' — where human perception, memory, and cognition are co-created by both biology and circuitry.

The more artificial intelligence advances, the more the human brain becomes an attractive model. The reason is simple: the brain is not perfect. It tolerates errors, interprets noise, and learns from context. Computers, by contrast, are precise but meaningless — flawless in logic, but devoid of emotion or imagination.

That is the essence of neuromorphic circuits: they do not seek perfect calculation, but rather an understanding of imperfect thought. The idea that electrons can simulate emotion or that circuits can possess memory may sound like fiction, yet several studies now report that artificial neurons exhibit 'forgetting' and 'stabilization' behaviors — mirroring the brain’s own process of synaptic plasticity, strengthening and weakening over time.

In short, the computers of the future will not simply be faster machines. They will become systems that 'adapt, interpret, and contextualize' information. The future of computation lies not in speed, but in meaning.

The impact of this technology reaches far beyond the improvement of AI efficiency. In artificial intelligence, robotics, the metaverse, medicine, and education, it will transform the very relationship between humans and machines.

With low-power, high-speed, brain-like computation, robots will interpret human intention more intuitively, and AI will respond not only to words but to emotional context. In medicine, damaged neurons could be replaced by artificial ones to treat neurological disorders, while brainwave stabilization devices for consciousness imbalance are already in testing. In education, brainwave-based learning interfaces are on the verge of commercialization.

Humanity is thus moving from an era of 'using' technology to an era where technology 'understands' us.

A device that retains memory even without current, that changes its response when stimulation is repeated — the artificial neuron stands between life and matter. It is not merely a semiconductor; it is a liminal form between biology and physics.

Scientists in 2025 no longer seek to simply copy the human brain. Instead, they explore the 'dialogue' between life and material — not translating neural signals into digital code, but enabling electrons themselves to learn sense and meaning.

Now, human thought flows across circuits, and the senses of electrons begin to resemble the human mind. Intelligence is not code — it is connection. The age of AI is, ultimately, the age where 'machines feel and humans understand.'

Zhou, L. et al. (2025). 'Artificial Neurons Mimicking Ion-Based Spiking Dynamics for Neuromorphic and Brain–Machine Interfaces.' 'Nature', March 2025.