BDNF‐Regulated Molecular‐to‐Material Transduction in an Ion–Electronic Memristive Biointerface for Modeling Addiction‐Related Neuroplasticity

ABSTRACT Addiction‐related memories are associated with dysregulated neurotrophic signaling and maladaptive synaptic plasticity. However, it still remains challenging in translating such disease‐relevant molecular regulation into history‐dependent and programmable material states. Herein, a brain‐derived neurotrophic factor (BDNF)‐regulated ion–electronic memristive biointerface is developed by integrating a recognition module comprising a BDNF‐responsive aptamer and a tropomyosin receptor kinase B Fc chimera (TrkB‐Fc) with a photoactive sodium alginate (SA)/WO 3 composite. Under 365 nm illumination, BDNF‐dependent interfacial regulation produces concentration‐ and history‐dependent conductance dynamics associated with W 6+ /W 5+ mixed‐valence evolution. The device exhibits synaptic‐like responses, including paired‐pulse facilitation, learning–forgetting–relearning‐like dynamics, and reversible potentiation/depression‐like conductance modulation. These device‐state changes show functional correspondence with BDNF‐enhanced ΔFosB expression and morphine‐conditioned place preference, whereas ANA‐12 attenuates device‐state persistence and behavioral reinstatement in the respective experiments. Experimentally measured volatile responses are further used to construct reservoir states, while experimentally characterized nonvolatile conductance states provide a device‐informed basis for differential‐conductance readout‐weight representation in a software‐executed classification workflow, achieving a held‐out Fashion‐MNIST accuracy of 93.9%. Therefore, this work establishes a molecular‐to‐material transduction framework that converts neurotrophic regulation into programmable ion–electronic states and links biomolecular recognition, adaptive material dynamics, addiction‐related neuroplasticity, and proof‐of‐concept neuromorphic information processing.

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Publication Details

Journal
Advanced Functional Materials
Published
2026-11-01
DOI
https://doi.org/10.1002/adfm.79338
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

BDNF‐Regulated Molecular‐to‐Material Transduction in an Ion–Electronic Memristive Biointerface for Modeling Addiction‐Related Neuroplasticity

Wentao Hou, Bai Tao Sun, Dongyu Yu, Yu-Xiang Zhang et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

BDNF‐Regulated Molecular‐to‐Material Transduction in an Ion–Electronic Memristive Biointerface for Modeling Addiction‐Related Neuroplasticity

Wentao Hou, Bai Tao Sun, Dongyu Yu, Yu-Xiang Zhang, Song Ling Wang, Zhuojin Yang, Chunxia Yan, Junchao Zhang, Yuyanran Zhang, Jie Chen, Beiling Hou, Xixi Yang
article en

Abstract

ABSTRACT Addiction‐related memories are associated with dysregulated neurotrophic signaling and maladaptive synaptic plasticity. However, it still remains challenging in translating such disease‐relevant molecular regulation into history‐dependent and programmable material states. Herein, a brain‐derived neurotrophic factor (BDNF)‐regulated ion–electronic memristive biointerface is developed by integrating a recognition module comprising a BDNF‐responsive aptamer and a tropomyosin receptor kinase B Fc chimera (TrkB‐Fc) with a photoactive sodium alginate (SA)/WO 3 composite. Under 365 nm illumination, BDNF‐dependent interfacial regulation produces concentration‐ and history‐dependent conductance dynamics associated with W 6+ /W 5+ mixed‐valence evolution. The device exhibits synaptic‐like responses, including paired‐pulse facilitation, learning–forgetting–relearning‐like dynamics, and reversible potentiation/depression‐like conductance modulation. These device‐state changes show functional correspondence with BDNF‐enhanced ΔFosB expression and morphine‐conditioned place preference, whereas ANA‐12 attenuates device‐state persistence and behavioral reinstatement in the respective experiments. Experimentally measured volatile responses are further used to construct reservoir states, while experimentally characterized nonvolatile conductance states provide a device‐informed basis for differential‐conductance readout‐weight representation in a software‐executed classification workflow, achieving a held‐out Fashion‐MNIST accuracy of 93.9%. Therefore, this work establishes a molecular‐to‐material transduction framework that converts neurotrophic regulation into programmable ion–electronic states and links biomolecular recognition, adaptive material dynamics, addiction‐related neuroplasticity, and proof‐of‐concept neuromorphic information processing.

Advanced Functional Materials
Chinese Academy of Sciences (CN), Fujian Institute of Research on the Structure of Matter (CN), Stomatology Hospital (CN), State Key Laboratory of Structural Chemistry (CN), Zhejiang University of Technology (CN), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 24%
Advanced Memory and Neural Computing
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