KAIST researchers have developed a semiconductor neuron that turns one of electronics’ most troublesome imperfections—electrical noise—into a controllable tool for processing information. Instead of suppressing random fluctuations generated inside a memory device, the team has learned how to tune them and use them to determine when an artificial neuron fires. The result is a programmable probabilistic neuron that can be adjusted to recognize signals moving at very different speeds, from the slow patterns of human motion to the rapid frequencies of speech. In tests, the technology classified human activity signals with 94.8 percent accuracy and speech signals with 95.0 percent accuracy, suggesting that carefully managed randomness could become a powerful feature in next-generation artificial intelligence hardware.
The work, led by Professor Kyung Min Kim of the Department of Materials Science and Engineering at the Korea Advanced Institute of Science and Technology, addresses a fundamental difference between conventional computers and biological brains. Digital electronics are generally designed to produce stable, repeatable outputs. Unwanted variations in current or voltage are treated as noise because they can distort data and reduce computational precision. The brain, however, does not operate as a perfectly deterministic machine. Biological neurons may fire at different times, or with different frequencies, even when they receive similar stimuli. These variations arise partly from the stochastic opening and closing of ion channels in neuronal membranes. Rather than simply degrading performance, that probabilistic behavior helps biological neural networks adapt to uncertain and changing environments.
The KAIST team sought to reproduce this useful biological irregularity in a compact semiconductor device. Their platform is based on a memristor, an electronic component whose resistance changes in response to an applied electrical stimulus and can retain that resistance after the stimulus is removed. Because memristors can both store information and participate in computation, they are widely viewed as promising building blocks for neuromorphic systems designed to imitate aspects of brain function. In ordinary memristor circuits, however, fluctuations in current are often regarded as an obstacle, or they are exploited mainly to generate random numbers and support probabilistic computing. The researchers instead asked whether the noise itself could be deliberately shaped and assigned a functional role in signal processing.
Their central discovery was that the noise produced by a memristor is not fixed. Its magnitude and behavior change when the device is placed in a different resistance state. By setting that state in advance, the researchers could alter how likely the device was to produce a spike in response to a given input. A spike is the brief electrical event used by artificial neurons to represent and transmit information. In the new system, the same input can therefore lead to different firing probabilities depending on the memristor’s programmed resistance. This creates a reconfigurable artificial neuron whose response is not only adjustable in strength, but also probabilistic in a way that resembles the variable firing patterns of biological sensory neurons.
The researchers named their device a programmable probabilistic neuron, or PPN. Its operation depends on converting current fluctuations into meaningful spike patterns rather than treating those fluctuations as errors. When an input signal crosses the neuron’s effective response conditions, the noise can help determine whether and when a spike is generated. Repeated presentations of a similar signal do not necessarily produce identical spike trains, but the statistical properties of those trains can be controlled. In technical terms, programming the memristor modifies the neuron’s firing probability and input response range. This gives the circuit a tunable relationship between the temporal characteristics of a signal and the electrical events used to encode it.
That tunability is especially important for time-series data, in which information is carried not only by the size of a signal but also by how quickly it changes. Human activity signals, such as those captured by wearable sensors, generally evolve over relatively slow time scales and occupy frequency ranges around hertz. Speech contains much faster variations, extending into the kilohertz range, where one kilohertz represents 1,000 cycles per second. Conventional hardware may require different filters, processing pathways, or neural circuits to handle such distinct signal regimes. The KAIST approach allows the same neuron architecture to be reconfigured simply by changing the memristor’s resistance state, enabling it to become more responsive to slow or fast inputs without replacing the underlying circuit.
In demonstrations, the team adjusted the artificial neuron for different frequency-selective tasks. With one configuration, the system processed signals associated with human movement and activity. With another, it responded to the more rapidly changing patterns found in speech. The resulting system encoded and classified the two categories of time-series information with accuracies of 94.8 percent for human activity recognition and 95.0 percent for speech recognition. These results indicate that the noise-tuning mechanism can influence not only the behavior of an isolated device, but also the performance of a practical signal-processing pipeline. The technology could be particularly valuable in edge devices, where data must be interpreted locally rather than transmitted continuously to a remote server.
The appeal of this approach extends beyond its recognition accuracy. Neuromorphic hardware is being developed to reduce the energy and latency costs associated with conventional artificial intelligence, especially in sensors, wearable electronics, robotics, and autonomous machines. In many current systems, sensor data are converted into digital form and moved through several layers of memory and computation, a process that consumes energy and introduces delays. Memristor-based neurons could perform aspects of sensing, memory, and computation closer to where data are generated. Because the KAIST neuron can be tuned for different signal speeds through a device-level resistance adjustment, one hardware platform might support multiple applications while reducing the need for separate specialized circuits.
Professor Kim described the significance of the work as a shift in how semiconductor noise is understood. Instead of treating noise solely as an indicator of instability or a source of computational error, the researchers demonstrated that it can be used as a programmable information-processing resource. The concept also reflects an important principle of biological computation: variability does not always have to be eliminated to achieve reliable behavior. When controlled statistically, randomness can help a system distinguish patterns, respond flexibly, and operate across changing conditions. The researchers say that future versions of the technology could contribute to low-power neuromorphic systems capable of processing diverse sensory signals with adaptable hardware.
The study was led by Dr. Do Hoon Kim as first author and was published in Advanced Materials under the title “Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding.” The research was supported by Korea’s Basic Research Program in Science and Engineering and the PIM Artificial Intelligence Semiconductor Core Technology Development Program, funded through the Ministry of Science and ICT and the National Research Foundation of Korea. Although further work will be needed to assess long-term reliability, large-scale integration, and performance under real-world conditions, the findings point to a provocative future for electronic noise: rather than an enemy that every circuit must silence, it may become one of the mechanisms that allows intelligent machines to sense and interpret the world.
Subject of Research:
Noise-tunable memristor-based programmable probabilistic neurons for frequency-selective time-series signal processing and neuromorphic computing.
Article Title:
Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding
News Publication Date:
August 16
Web References:
https://doi.org/10.1002/adma.74529
https://mediasvc.eurekalert.org/Api/v1/Multimedia/833f7795-897f-44eb-969a-97c2c7e4e1c3/Rendition/low-res/Content/Public
References:
Kim, Do Hoon et al., “Noise-Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency-Selective Time-Series Signal Encoding,” Advanced Materials, DOI: 10.1002/adma.74529.
Image Credits:
KAIST
Keywords
Memristor, probabilistic neuron, neuromorphic computing, semiconductor noise, artificial intelligence, brain-inspired technology, time-series signal processing, frequency-selective encoding, human activity recognition, speech recognition, edge AI, low-power electronics, KAIST.
