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Related Experiment Video

Updated: Jan 12, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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A Reconfigurable Silicon Transistor for Noise-Resilient Stochastic Spiking Neural Networks.

Ho-Young Maeng1, Hyeonji Lee1, Sang-Won Lee1

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

ACS Nano
|November 7, 2025
PubMed
Summary

This study introduces a novel neuronal transistor (neuristor) for stochastic spiking neural networks (SSNNs). This device enhances noise resilience and adaptability in neuromorphic systems by integrating dual stochastic and deterministic functionalities.

Keywords:
leaky integrate-and-fire (LIF)neuronal transistor (neuristor)noise resiliencesingle transistor latch (STL)stochastic encodingstochastic spiking neural network (SSNN)

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computational Neuroscience

Background:

  • Spiking neural networks (SNNs) offer energy efficiency but lack biological stochasticity, limiting noise resilience.
  • Stochastic SNNs (SSNNs) address this by incorporating probabilistic behavior for enhanced noise tolerance and adaptability.

Purpose of the Study:

  • To develop a novel neuronal transistor (neuristor) with dual stochastic and deterministic properties for advanced SSNNs.
  • To demonstrate the neuristor's capability for noise-resilient and energy-efficient neuromorphic computing.

Main Methods:

  • Reengineered conventional CMOS technology to create a neuristor based on silicon and its derivatives.
  • Integrated stochastic encoding (input layer) and Leaky Integrate-and-Fire (LIF) behavior (hidden/output layers) within a single neuristor device.
  • Utilized the single transistor latch (STL) mechanism for dual-mode operation via impact ionization (stochastic) and charge accumulation (LIF).

Main Results:

  • The neuristor successfully integrated dual stochastic and deterministic functionalities within a single device.
  • A neuristor-based SSNN achieved 92% classification accuracy on the MNIST dataset with 30% Gaussian noise.
  • Demonstrated strong noise resilience and potential for scalable, biologically inspired neuromorphic systems.

Conclusions:

  • The developed neuristor enables reliable and noise-resilient SSNNs through reconfigurable dual-mode operation.
  • This innovation simplifies circuit design and enhances the scalability of energy-efficient neuromorphic systems.
  • The neuristor-based SSNN shows significant promise for practical applications requiring robustness against noise.