SnNb <sub>2</sub> O <sub>6</sub> -Based Capacitive Memristive Synapses with Intrinsic LIF Dynamics for Neuromorphic Epilepsy Detection

Integrating tunable leaky-integrate-and-fire (LIF) dynamics with a synaptic memristor holds profound promise in the early diagnosis of neurological disorders. Here, a SnNb<sub>2</sub>O<sub>6</sub>-based capacitive memristor is presented, which emulates a biosynaptic neuron through an LIF-type excitatory postsynaptic current (EPSC) for epileptic seizure monitoring. The device functions with stable coupled bipolar switching and capacitive response under both DC and pulsed operation over 500 consecutive cycles; in particular, single-pulse stimulus triggers a highly reproducible LIF-type three-stage EPSC feature. Various stimulation protocols enable diverse synaptic behaviors, and the device response can transition from transient to persistent states by tuning readout-delay. Mechanistic analysis attributes the capacitive memristor behavior to the coupling effect between interfacial polarization/displacement current and dynamic Schottky barrier modulation. By employing measured LIF-type EPSC as convolution kernels for raw electroencephalogram (EEG) signals in a lightweight one-dimensional convolutional neural network (1D-CNN), a 95.8% classification accuracy for three epilepsy-related states is achieved on the Bonn dataset. The results highlight the potential of the capacitive memristor for on-chip epileptic seizure monitoring.

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Publication Details

Journal
Nano Letters
Published
2026-01-08
DOI
https://doi.org/10.1021/acs.nanolett.5c05639
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

SnNb 2 O 6 -Based Capacitive Memristive Synapses with Intrinsic LIF Dynamics for Neuromorphic Epilepsy Detection

Xiaofei Dong, Yan Li, Xuqiang Zhang, Siyuan Li et al.
Nano Letters
Advanced Memory and Neural Computing
article

SnNb 2 O 6 -Based Capacitive Memristive Synapses with Intrinsic LIF Dynamics for Neuromorphic Epilepsy Detection

Xiaofei Dong, Yan Li, Xuqiang Zhang, Siyuan Li, Zhi Zhan, Yuanzhang Zhao, Jiangtao Chen, Jianbiao Chen, Fengxia Yang, Xiang Zhang, Lin Ge, Hao Sun
article en

Abstract

Integrating tunable leaky-integrate-and-fire (LIF) dynamics with a synaptic memristor holds profound promise in the early diagnosis of neurological disorders. Here, a SnNb<sub>2</sub>O<sub>6</sub>-based capacitive memristor is presented, which emulates a biosynaptic neuron through an LIF-type excitatory postsynaptic current (EPSC) for epileptic seizure monitoring. The device functions with stable coupled bipolar switching and capacitive response under both DC and pulsed operation over 500 consecutive cycles; in particular, single-pulse stimulus triggers a highly reproducible LIF-type three-stage EPSC feature. Various stimulation protocols enable diverse synaptic behaviors, and the device response can transition from transient to persistent states by tuning readout-delay. Mechanistic analysis attributes the capacitive memristor behavior to the coupling effect between interfacial polarization/displacement current and dynamic Schottky barrier modulation. By employing measured LIF-type EPSC as convolution kernels for raw electroencephalogram (EEG) signals in a lightweight one-dimensional convolutional neural network (1D-CNN), a 95.8% classification accuracy for three epilepsy-related states is achieved on the Bonn dataset. The results highlight the potential of the capacitive memristor for on-chip epileptic seizure monitoring.

Nano LettersVol. 26(2)
Institute of Molecular Functional Materials (CN), Northwest Normal University (CN)
Openalex Percentile: Top 97%
Advanced Memory and Neural Computing
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SnNb <sub>2</sub> O <sub>6</sub> -Based Capacitive Memristive Synapses with Intrinsic LIF Dynamics for Neuromorphic Epilepsy Detection — Xiaofei Dong, Yan Li, et al. · Nano Letters (2026) | TGRS Research Map | TGRS