Fiber Transistors as a Hardware Surrogate Gradient for Backpropagation in Spiking Neural Networks
Abstract
Abstract Efficient training of spiking neural networks (SNNs) in flexible neuromorphic hardware remains a major challenge due to the non‐differentiable nature of spiking activations and the limited availability of trainable, energy‐efficient device platforms. Here, a tunable textile‐based vertical organic electrochemical transistor (TT‐vOECT) enables implementation of surrogate gradients computation for backpropagation in SNNs. Using a solvent interdiffusion solidification spinning strategy, Plateau–Rayleigh instabilities are overcome to fabricate coaxial trilayer fibers with well‐defined heterojunctions between PEDOT:PSS and BBL. The resulting TT‐vOECT exhibits nonlinear transfer characteristics that closely approximate the Sigmoid derivative. A conditionally activated backpropagation (CAB) mechanism is further proposed, in which synaptic updates are gated by both surrogate gradient magnitude and input spike, is implemented using reconfigurable logic arrays based on dual TT‐vOECTs. This framework enables sparse, event‐driven weight updates with reduced computational overhead. Integrated into a convolutional SNN, the device‐driven CAB strategy achieves high‐accuracy classification of electroencephalogram signals for multiclass neurological disorder diagnosis, comparable to software baselines, while reducing computational redundancy by ≈20%. The results establish a scalable and flexible organic hardware platform for trainable neuromorphic systems with biologically inspired learning dynamics.
Article Details
Authors (16)
Weichu Chen
State Key Laboratory of Advanced Fiber Materials College of Materials Science and Engineering Donghua University Shanghai China
Hao Jiang
Chengyang Du
Shanghai Key Laboratory of Multidimensional Information Processing School of Communication & Electronic Engineering East China Normal University Shanghai 200241 China
Yueheng Zhong
State Key Laboratory of Advanced Fiber Materials College of Materials Science and Engineering Donghua University Shanghai China
Xiangyu Wang
Xiang Li
Zhu Chen
Key Laboratory of Biomedical Polymers of Ministry of Education, Department of Chemistry, Department of Cardiology, Zhongnan Hospital
Qicheng Liang
State Key Laboratory of Advanced Fiber Materials College of Materials Science and Engineering Donghua University Shanghai China
Fengqiang Sun
State Key Laboratory of Advanced Fiber Materials College of Materials Science and Engineering Donghua University Shanghai 201620 China
Yuwen Zhu
University of Colorado Anschutz School of Medicine
Jian‐Gang Chen
Shanghai Key Laboratory of Multidimensional Information Processing School of Communication & Electronic Engineering East China Normal University Shanghai 200241 China
Liang‐Wen Feng
Key Laboratory of Green Chemistry & Technology Ministry of Education College of Chemistry Sichuan University Chengdu China
Hongzhi Wang
Meifang Zhu
Hengda Sun
State Key Laboratory of Advanced Fiber Materials College of Materials Science and Engineering Shanghai Key Laboratory of Lightweight Composite Key Laboratory of High Performance Fibers & Products Donghua University Shanghai People's Republic of China
Gang Wang