Inhibitory neuristor based on metal-to-insulator transition
Pith reviewed 2026-05-10 01:31 UTC · model grok-4.3
The pith
Metal-to-insulator transition devices produce inhibitory self-oscillations when placed in simple RL circuits.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Electrical triggering of the metal-to-insulator transition suppresses current flow in two-terminal switching devices, producing inhibitory-like behavior. When these devices are incorporated into a simple RL circuit, the suppression leads to robust self-oscillations at frequencies of roughly 0.1 to 1 MHz with low cycle-to-cycle variation. The oscillations can be tuned by varying the applied DC voltage, temperature, and inductance, establishing a functional inhibitory MIT-based artificial neuron that complements existing excitatory IMT devices.
What carries the argument
The metal-to-insulator transition (MIT) in two-terminal devices inside an RL circuit, which abruptly reduces conductivity and thereby generates self-sustained current suppression and oscillations.
Load-bearing premise
That the observed current suppression and oscillations are produced by the metal-to-insulator transition mechanism itself rather than by other circuit parasitics or material properties.
What would settle it
If the same circuit fails to oscillate or shows no current suppression when the device is held at a temperature where the metal-to-insulator transition is prevented, the claim that the MIT produces the inhibitory dynamics would be falsified.
Figures
read the original abstract
Mimicking the collective excitatory and inhibitory behaviors of biological neurons remains a critical challenge in the development of neuromorphic computing systems that rival the complexity and performance of the human brain. Volatile high-to-low resistance switching in insulator-to-metal transition (IMT) materials produces an abrupt increase in current flow, resembling neuronal excitation. This electrical excitation enables IMT materials to be driven into a neuron-like spiking self-oscillation regime using simple RC circuits. Here, we report a new type of self-oscillation dynamics that occurs in the opposite class of metal-to-insulator transition (MIT) materials. Electrical triggering of the MIT suppresses current flow, resembling neuronal inhibition. Using a prototypical MIT material, we experimentally demonstrate inhibitory-like self-oscillations in two-terminal switching devices incorporated into a simple RL circuit. Our results show robust ~0.1 - 1 MHz electric current oscillations with minimal cycle-to-cycle variation, which can be controlled by varying the applied DC voltage, temperature, and inductance. This work demonstrates a new type of inhibitory MIT-based artificial neuron that can complement the excitatory functionalities of IMT-based neuristors in biologically plausible neuromorphic systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript experimentally demonstrates inhibitory-like self-oscillations in two-terminal metal-to-insulator transition (MIT) devices placed in a simple RL circuit. Using a prototypical MIT material, the authors report robust current oscillations in the ~0.1-1 MHz range with low cycle-to-cycle variation; these oscillations are shown to be controllable by DC bias voltage, temperature, and circuit inductance. The work positions the MIT-based device as a complementary inhibitory neuristor to existing insulator-to-metal transition (IMT) excitatory devices for neuromorphic hardware.
Significance. If the central experimental claims are substantiated, the result supplies a missing hardware primitive for inhibitory dynamics in neuromorphic circuits. The use of a minimal RL circuit and the reported frequency range (relevant for MHz-scale operation) are practical advantages. Explicit control via voltage, temperature, and inductance provides a clear route to tuning, which strengthens the case for integration into larger excitatory-inhibitory networks.
major comments (2)
- [Results] Results section (oscillation data): The abstract and main text assert 'minimal cycle-to-cycle variation' and 'robust' oscillations, yet no quantitative metrics (standard deviation of period, coefficient of variation, or cycle histograms) or error bars on the reported frequency range are provided. Without these, the robustness claim cannot be evaluated against the central assertion of reliable inhibitory behavior.
- [Methods / Device characterization] Device characterization (methods or supplementary): The attribution of the observed current suppression specifically to the MIT mechanism requires explicit confirmation that the devices exhibit the expected temperature-driven resistivity jump and that oscillations cease outside the transition window. The current description does not include such control experiments or I-V-T curves that would rule out parasitic relaxation-oscillator behavior unrelated to the phase transition.
minor comments (2)
- [Figures] Figure captions should explicitly state the number of cycles averaged or the total observation time used to generate the time-series plots.
- [Results] The frequency range is given as '~0.1 - 1 MHz'; a table or plot showing the exact dependence on inductance and voltage would make the controllability claim more precise.
Simulated Author's Rebuttal
We thank the referee for their constructive comments and positive assessment of the significance of our experimental demonstration of inhibitory self-oscillations in MIT devices. We address each major comment below and have revised the manuscript to incorporate additional quantitative analysis and characterization data.
read point-by-point responses
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Referee: [Results] Results section (oscillation data): The abstract and main text assert 'minimal cycle-to-cycle variation' and 'robust' oscillations, yet no quantitative metrics (standard deviation of period, coefficient of variation, or cycle histograms) or error bars on the reported frequency range are provided. Without these, the robustness claim cannot be evaluated against the central assertion of reliable inhibitory behavior.
Authors: We agree that quantitative metrics are required to rigorously support the claims of robustness and minimal cycle-to-cycle variation. In the revised manuscript, we have added explicit analysis of oscillation stability, including the coefficient of variation and standard deviation of the period computed over hundreds of cycles, cycle duration histograms, and error bars on the reported frequency range derived from repeated measurements under identical conditions. These additions allow direct evaluation of the reliability of the inhibitory behavior. revision: yes
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Referee: [Methods / Device characterization] Device characterization (methods or supplementary): The attribution of the observed current suppression specifically to the MIT mechanism requires explicit confirmation that the devices exhibit the expected temperature-driven resistivity jump and that oscillations cease outside the transition window. The current description does not include such control experiments or I-V-T curves that would rule out parasitic relaxation-oscillator behavior unrelated to the phase transition.
Authors: We acknowledge the value of explicit controls to confirm the MIT origin. The revised supplementary information now includes I-V-T curves for the devices that clearly show the temperature-driven resistivity jump characteristic of the MIT. We have also added control data demonstrating that self-oscillations are absent when the temperature is set outside the MIT transition window, which helps exclude unrelated parasitic relaxation-oscillator mechanisms. These revisions directly tie the observed current suppression to the phase transition. revision: yes
Circularity Check
No significant circularity: experimental demonstration only
full rationale
The paper reports an experimental demonstration of inhibitory self-oscillations in two-terminal MIT devices placed in a simple RL circuit. Claims rest on observed current oscillations (~0.1-1 MHz) controlled by DC voltage, temperature, and inductance, with no mathematical derivations, equations, fitted parameters, or uniqueness theorems presented. No self-citations are invoked to justify ansatzes or forbid alternatives; the work is self-contained as a direct measurement of device behavior in a circuit. The absence of any derivation chain means no opportunity for self-definitional, fitted-input, or self-citation circularity exists.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
-
[1]
M. D. Pickett, G. Medeiros-Ribeiro, and R. S. Williams, A scalable neuristor built with Mott memristors, Nat. Mater. 12, 114 (2013)
work page 2013
-
[2]
W. Yi, K. K. Tsang, S. K. Lam, X. Bai, J. A. Crowell, and E. A. Flores, Biological plausibility and stochasticity in scalable VO2 active memristor neurons, Nat. Commun. 9, 4661 (2018)
work page 2018
-
[3]
S. M. Bohaichuk, S. Kumar, G. Pitner, C. J. McClellan, J. Jeong, M. G. Samant, H.-. S. P. Wong, S. S. P. Parkin, R. S. Williams, and E. Pop, Fast Spiking of a Mott VO2–Carbon Nanotube Composite Device, Nano Lett. 19, 6751 (2019)
work page 2019
-
[4]
J. del Valle, P. Salev, Y . Kalcheim, and I. K. Schuller, A caloritronics-based Mott neuristor, Sci. Rep. 10, 4292 (2020)
work page 2020
-
[5]
C. Y . Han et al., A Flexible Artificial Spiking Photoreceptor Enabled by a Single VO2 Mott Memristor for the Spike-Based Electronic Retina, ACS Appl. Mater. Interfaces 16, 57404 (2024)
work page 2024
-
[6]
Y. X i a o e t a l . , B i o-plausible reconfigurable spiking neuron for neuromorphic computing, Sci. Adv. 11, eadr6733 (2025)
work page 2025
-
[7]
Y. Wa n g , X . Wa n g , J . Z h a n g , Q . L i n , a n d H . X u , P h a s e-Change VO2 Enabled Optoelectronic Devices With Switching Dynamics, Adv. Opt. Mater. 13, 1 (2025)
work page 2025
- [8]
-
[9]
Qiu et al., Stochasticity in the synchronization of strongly coupled spiking oscillators, Appl
E. Qiu et al., Stochasticity in the synchronization of strongly coupled spiking oscillators, Appl. Phys. Lett. 9 122, 094105 (2023)
work page 2023
-
[10]
E. Qiu, P. Salev, F. Torres, H. Navarro, R. C. Dynes, and I. K. Schuller, Stochastic transition in synchronized spiking nanooscillators, Proceedings of the National Academy of Sciences 120, e2303765120 (2023)
work page 2023
-
[11]
Yang et al., High-order sensory processing nanocircuit based on coupled VO2 oscillators, Nat
K. Yang et al., High-order sensory processing nanocircuit based on coupled VO2 oscillators, Nat. Commun. 15, 1693 (2024)
work page 2024
-
[12]
G. Li, Z. Wang, Y . Chen, J.-C. Jeon, and S. S. P. Parkin, Computational elements based on coupled VO2 oscillators via tunable thermal triggering, Nat. Commun. 15, 5820 (2024)
work page 2024
- [13]
- [14]
-
[15]
R. Yuan et al., A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system, Nat. Commun. 13, 3973 (2022)
work page 2022
-
[16]
L. Wu et al., Implementation of Neuronal Intrinsic Plasticity by Oscillatory Device in Spiking Neural Network, IEEE Trans. Electron Devices 69, 1830 (2022)
work page 2022
-
[17]
R. Yuan, P. J. Tiw, L. Cai, Z. Yang, C. Liu, T. Zhang, C. Ge, R. Huang, and Y . Yang, A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface, Nat. Commun. 14, 3695 (2023)
work page 2023
-
[18]
J. del Valle, J. G. Ramírez, M. J. Rozenberg, and I. K. Schuller, Challenges in materials and devices for resistive-switching-based neuromorphic computing, J. Appl. Phys. 124, 211101 (2018)
work page 2018
-
[19]
A. Milloch, M. Fabrizio, and C. Giannetti, Mott materials: unsuccessful metals with a bright future, Npj Spintronics 2, 49 (2024)
work page 2024
- [20]
- [21]
-
[22]
T. Hennen, D. Bedau, J. A. J. Rupp, C. Funck, S. Menzel, M. Grobis, R. Waser, and D. J. Wouters, Switching Speed Analysis and Controlled Oscillatory Behavior of a Cr-Doped V2O3 Threshold Switching Device for Memory Selector and Neuromorphic Computing Application, in 2019 IEEE 11th International Memory Workshop (IMW) (IEEE, 2019)
work page 2019
-
[23]
S. M. Bohaichuk, S. Kumar, G. Pitner, C. J. McClellan, J. Jeong, M. G. Samant, H.-. S. P. Wong, S. S. P. Parkin, R. S. Williams, and E. Pop, Fast Spiking of a Mott VO2 –Carbon Nanotube Composite Device, Nano Lett. 19, 6751 (2019)
work page 2019
-
[24]
H. Liu et al., A Tantalum Disulfide Charge-Density-Wave Stochastic Artificial Neuron for Emulating Neural Statistical Properties, Nano Lett. 21, 3465 (2021)
work page 2021
-
[25]
C. Adda et al., Direct Observation of the Electrically Triggered Insulator-Metal Transition in V3O5 Far below the Transition Temperature, Phys. Rev. X 12, 011025 (2022)
work page 2022
-
[26]
U. Khandelwal, Q. Guo, B. Noheda, P. Nukala, and S. Chandorkar, Dynamics of V oltage-Driven Self-Sustained Oscillations in NdNiO3 Neuristors, ACS Appl. Electron. Mater. 5, 3859 (2023)
work page 2023
-
[27]
S. K. Das et al., Physical Origin of Negative Differential Resistance in V3O5 and Its Application as a Solid‐ 10 State Oscillator, Advanced Materials 35, 2208477 (2023)
work page 2023
-
[28]
S. K. Nath et al., Optically Tunable Electrical Oscillations in Oxide‐Based Memristors for Neuromorphic Computing, Advanced Materials 36, 2400904 (2024)
work page 2024
-
[29]
E. Dagotto, T. Hotta, and A. Moreo, Colossal magnetoresistant materials: the key role of phase separation, Phys. Rep. 344, 1 (2001)
work page 2001
-
[30]
Y. To k u r a , C r i t i c a l f e a t u r e s o f c o l o s s a l m a g n e t o r e s i s t i v e m a n g a n i t e s , R e p o r t s o n P r o g r e s s i n P h y s i c s 69, 797 (2006)
work page 2006
- [31]
- [32]
- [33]
-
[34]
E. Kisiel et al., High-Resolution Full-Field Structural Microscopy of the V oltage-Induced Filament Formation in VO2-Based Neuromorphic Devices, ACS Nano 19, 15385 (2025)
work page 2025
-
[35]
E. Salagre et al., Electrothermally Induced Channel Formation in a Spin-Crossover Neuron, ACS Nano 20, 6915 (2026)
work page 2026
-
[36]
P. Salev et al., Local strain inhomogeneities during electrical triggering of a metal–insulator transition revealed by X-ray microscopy, Proceedings of the National Academy of Sciences 121, e2317944121 (2024)
work page 2024
- [37]
- [38]
-
[39]
T.-Y. C h e n , H . R e n , N . G h a z i k h a n i a n , R . E l H a g e , D . Y. S a s a k i , P. S a l e v, Y. Ta k a m u r a , I . K . S c h u l l e r, a n d A . D. Kent, Electrical Control of Magnetic Resonance in Phase Change Materials, Nano Lett. 24, 11476 (2024)
work page 2024
-
[40]
T.-Y. C h e n , D . Y. S a s a k i , B . A c h i n u q , N . G h a z i k h a n i a n , P. S a l e v, H . O h l d a g, A. Scholl, I. K. Schuller, Y . Takamura, and A. D. Kent, Voltage-induced magnetic domain evolution in a phase-change material, Appl. Phys. Lett. 125, 262406 (2024)
work page 2024
-
[41]
M. Tokunaga, Y . Tokunaga, and T. Tamegai, Imaging of Percolative Conduction Paths and Their Breakdown in Phase-Separated (La1-yPry)0.7Ca0.3MnO3 with y = 0:7, Phys. Rev. Lett. 93, 037203 (2004)
work page 2004
- [42]
-
[43]
A. S. Carneiro, R. F. Jardim, and F. C. Fonseca, Current localization and Joule self-heating effects in Cr-doped Nd0.5Ca0.5MnO3 manganites, Phys. Rev. B Condens. Matter Mater. Phys. 73, 012410 (2006)
work page 2006
-
[44]
S. Balevičius, N. Žurauskienė, V . Stankevič, P. Cimmperman, S. Keršulis, A. Česnys, S. Tolvaišienė, and L. L. Altgilbers, Fast reversible thermoelectrical switching in manganite thin films, Appl. Phys. Lett. 90, 212503 (2007)
work page 2007
-
[45]
Y. Ta k a m u r a , R . V C h o p d e k a r, E . A r e n h o l z , a n d Y. S u z u k i , C o n t r o l o f t h e m a g n e t i c a n d m a g n e t o t r a n s p o r t properties of La0.67Sr0.33MnO3 thin films through epitaxial strain, Appl. Phys. Lett. 92, 162504 (2008). 11
work page 2008
-
[46]
F. Yang, N. Kemik, M. D. Biegalski, H. M. Christen, E. Arenholz, and Y . Takamura, Strain engineering to control the magnetic and magnetotransport properties of La0.67Sr0.33MnO3 thin films, Appl. Phys. Lett. 97, 092503 (2010)
work page 2010
-
[47]
A. Parihar, M. Jerry, S. Datta, and A. Raychowdhury, Stochastic IMT (Insulator-Metal-Transition) Neurons: An Interplay of Thermal and Threshold Noise at Bifurcation, Front. Neurosci. 12, 210 (2018)
work page 2018
- [48]
-
[49]
B. Bhar, A. Khanna, A. Parihar, S. Datta, and A. Raychowdhury, Stochastic Resonance in Insulator-Metal-Transition Systems, Sci. Rep. 10, 5549 (2020)
work page 2020
-
[50]
L. Bao et al., Tunable Stochastic Oscillator Based on Hybrid VO₂/TaOₓ Device for Compressed Sensing, IEEE Electron Device Letters 42, 102 (2021)
work page 2021
-
[51]
J. del Valle, R. Rocco, C. Domínguez, J. Fowlie, S. Gariglio, M. J. Rozenberg, and J.-M. Triscone, Dynamics of the electrically induced insulator-to-metal transition in rare-earth nickelates, Phys. Rev. B 104, 165141 (2021)
work page 2021
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