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.
Authors
- Xiaofei Dong
- Yan Li (ORCID: https://orcid.org/0000-0001-9121-7077)
- Xuqiang Zhang
- Siyuan Li (ORCID: https://orcid.org/0000-0002-0336-9220)
- Zhi Zhan
- Yuanzhang Zhao (ORCID: https://orcid.org/0009-0003-3613-9469)
- Jiangtao Chen
- Jianbiao Chen
- Fengxia Yang
- Xiang Zhang
- Lin Ge
- Hao Sun
Institutions
- Institute of Molecular Functional Materials (CN)
- Northwest Normal University (CN)
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
- Field-Weighted Citation Impact
- 0.00