Emulating Synaptic Events and Nociceptor via Organic–Inorganic Perovskite Threshold Switching Memristor

As artificial intelligence technology continuously advances, a growing number of bio-mimetic advanced electronic systems are rapidly emerging and being applied in various fields, including humanoid robots and tactile sensors. To effectively address progressively complex tasks and challenging work environments, integrating synaptic and nociceptive functions within a single device is crucial for enhancing the ability to perceive changes and respond accordingly to the external environment. Here, an organic-inorganic perovskite memristor that exhibits excellent volatile performance (ON/OFF ratio ≈10<sup>2</sup>, endurance > 10<sup>4</sup> cycles) is presented. The device effectively replicates typical synaptic functions, encompassing short- and long-term plasticity. Moreover, due to the switching delay characteristics, essential biological nociceptive features such as threshold, no adaptation, and sensitization are also demonstrated. Further, the perovskite artificial nociceptor is successfully integrated into a thermal nociceptive system. Overall, the fusion of synaptic and nociceptive behaviors paves the way for developing more efficient and versatile systems that can mimic intricate biological processes associated with sensory perception and pain sensation.

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Publication Details

Journal
Small Methods
Published
2025-06-29
DOI
https://doi.org/10.1002/smtd.202500542
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

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article

Emulating Synaptic Events and Nociceptor via Organic–Inorganic Perovskite Threshold Switching Memristor

Christoph Josef Brabec, Chaohui Li, Maria Antonietta Loi, Shenglan Hao et al.
1 citations
Small Methods
Advanced Memory and Neural Computing
0.73
article

Emulating Synaptic Events and Nociceptor via Organic–Inorganic Perovskite Threshold Switching Memristor

Christoph Josef Brabec, Chaohui Li, Maria Antonietta Loi, Shenglan Hao, Jingjing Tian, Andres Osvet, Jianchang Wu, Zhiqiang Xie, Bobo Tian, Lijun Chen, Long Cheng, Mingjie Feng, Junsheng Luo, Difei Zhang
article en
1 citations

Abstract

As artificial intelligence technology continuously advances, a growing number of bio-mimetic advanced electronic systems are rapidly emerging and being applied in various fields, including humanoid robots and tactile sensors. To effectively address progressively complex tasks and challenging work environments, integrating synaptic and nociceptive functions within a single device is crucial for enhancing the ability to perceive changes and respond accordingly to the external environment. Here, an organic-inorganic perovskite memristor that exhibits excellent volatile performance (ON/OFF ratio ≈10<sup>2</sup>, endurance > 10<sup>4</sup> cycles) is presented. The device effectively replicates typical synaptic functions, encompassing short- and long-term plasticity. Moreover, due to the switching delay characteristics, essential biological nociceptive features such as threshold, no adaptation, and sensitization are also demonstrated. Further, the perovskite artificial nociceptor is successfully integrated into a thermal nociceptive system. Overall, the fusion of synaptic and nociceptive behaviors paves the way for developing more efficient and versatile systems that can mimic intricate biological processes associated with sensory perception and pain sensation.

Small MethodsVol. 10(2)
Centre National de la Recherche Scientifique (FR), University of Electronic Science and Technology of China (CN), University of Groningen (NL), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Université Paris-Saclay (FR), CentraleSupélec (FR), Helmholtz Institute Erlangen-Nürnberg (DE), Laboratoire Structures, Propriétés et Modélisation des Solides (FR), East China Normal University (CN), South China University of Technology (CN)
Solar Technologies go Hybrid, Deutsche Forschungsgemeinschaft, China Scholarship Council, Erlangen Graduate School of Advanced Optical Technologies, Bayerisches Staatsministerium für Wissenschaft und Kunst
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
0.73
DOIOpen Access PDFOpenAlex
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