Rivalling Dynamic Adaptation of Intrinsic Neuronal Plasticity in Dual‐Mode Mott Memristor Electronic Neuron for Spiking Control
Despite the success of Mott memristors in emulating neuronal dynamics, their use in modeling intrinsic neuronal plasticity (INP) remains rare. Here, a dual‐mode Pt/V/TaO x /Pt Mott memristor is presented that achieves INP through resistance‐dependent threshold modulation, enabled by the coupling of...
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| Published in | Advanced functional materials |
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| Format | Journal Article |
| Language | English |
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25.07.2025
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| Abstract | Despite the success of Mott memristors in emulating neuronal dynamics, their use in modeling intrinsic neuronal plasticity (INP) remains rare. Here, a dual‐mode Pt/V/TaO x /Pt Mott memristor is presented that achieves INP through resistance‐dependent threshold modulation, enabled by the coupling of conductive filament formation and Mott transition. Excitatory and inhibitory pulses independently modulate spiking frequency, time‐to‐first‐spike, and leaky integrate‐and‐fire behavior without altering synaptic input. Notably, the device exhibits adaptive INP responses under sustained modulation, dynamically stabilizing spiking behavior and preventing excessive excitation or inhibition. It is further demonstrated that INP can compensate for synaptic failure and help the neuron recover periodic spiking. These findings establish a compact, intrinsically plastic artificial neuron capable of self‐regulating excitability, offering a scalable solution for next‐generation neuromorphic hardware. |
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| AbstractList | Despite the success of Mott memristors in emulating neuronal dynamics, their use in modeling intrinsic neuronal plasticity (INP) remains rare. Here, a dual‐mode Pt/V/TaO x /Pt Mott memristor is presented that achieves INP through resistance‐dependent threshold modulation, enabled by the coupling of conductive filament formation and Mott transition. Excitatory and inhibitory pulses independently modulate spiking frequency, time‐to‐first‐spike, and leaky integrate‐and‐fire behavior without altering synaptic input. Notably, the device exhibits adaptive INP responses under sustained modulation, dynamically stabilizing spiking behavior and preventing excessive excitation or inhibition. It is further demonstrated that INP can compensate for synaptic failure and help the neuron recover periodic spiking. These findings establish a compact, intrinsically plastic artificial neuron capable of self‐regulating excitability, offering a scalable solution for next‐generation neuromorphic hardware. |
| Author | Hsu, Kai‐Shin Chen, Jen‐Sue Chen, Chi‐Chein Chen, Shuai‐Ming Chen, Kuan‐Ting Shih, Li‐Chung Liao, Zih‐Siao Lin, Kuan‐Han |
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