A SUPERVISED STDP-BASED TRAINING ALGORITHM FOR LIVING NEURAL NETWORKS

被引：0

作者：

Zeng, Yuan ^{[1
]}

Devincentis, Kevin ^{[3
]}

Xiao, Yao ^{[4
]}

Ferdous, Zubayer Ibne ^{[1
]}

Guo, Xiaochen ^{[1
]}

Yan, Zhiyuan ^{[1
]}

Berdichevsky, Yevgeny ^{[1
,2
]}

机构：

[1] Lehigh Univ, Elect & Comp Engn Dept, Bethlehem, PA 18015 USA

[2] Lehigh Univ, Bioengn Dept, Bethlehem, PA 18015 USA

[3] Carnegie Mellon Univ, Elect & Comp Engn Dept, Pittsburgh, PA 15213 USA

[4] Univ Sci & Technol China, Sch Gifted Young, Hefei, Anhui, Peoples R China

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

基金：

美国国家科学基金会;

关键词：

Spiking neural network; Spike timing dependent plasticity; Supervised learning; Biological neural network;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection. Neural network models are inspired by biological neural networks, but they are optimized to perform machine learning tasks on digital computers. The proposed work explores the possibility of using living neural networks in vitro as the basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constraints. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition.

引用

页码：1154 / 1158

页数：5

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