ArticleThe journal of physical chemistry letters2022
Dynamic Instability and Time Domain Response of a Model Halide Perovskite Memristor for Artificial Neurons.
Article in The journal of physical chemistry letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.Neuroinformatics · 2026Article
- Te/FeNanomaterials (Basel, Switzerland) · 2026Article
- Versatile Metal Phthalocyanine-Based Memristive Nanowire Network: Unraveling the Dynamics of Digital to Analog Switching.Small science · 2026Article
- Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices.The journal of physical chemistry letters · 2024Review
- Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers.Journal of materials chemistry. C · 2024Article
- Resistive switching in benzylammonium-based Ruddlesden-Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing.Materials advances · 2024Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process.The journal of physical chemistry letters · 2023Article
- Electrical Charge Coupling Dominates the Hysteresis Effect of Halide Perovskite Devices.The journal of physical chemistry letters · 2023Article
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2 authors.
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Abstract
Memristors are candidate devices for constructing artificial neurons, synapses, and computational networks for brainlike information processing and sensory-motor autonomous systems. However, the dynamics of natural neurons and synapses are challenging and cannot be well reproduced with standard electronic components. Halide perovskite memristors operate by mixed ionic-electronic properties that may lead to replicate the live computation elements. Here we explore the dynamical behavior of a halide perovskite memristor model to evaluate the response to a step perturbation and the self-sustained oscillations that produce analog neuron spiking. As the system contains a capacitor and a voltage-dependent chemical inductor, it can mimic an action potential in response to a square current pulse. Furthermore, we discover a property that cannot occur in the standard two-dimensional model systems: a three-dimensional model shows a dynamical instability that produces a spiking regime without the need for an intrinsic negative resistance. These results open a new pathway to create spiking neurons without the support of electronic circuits.
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