# A Supervised STDP-based Training Algorithm for Living Neural Networks

Source: [A Supervised STDP-based Training Algorithm for Living Neural Networks](<https://www.sota2.com/research/paper/68fa5ed91e209131ce9e5aa1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:43:11.176Z

Authors: Yuan Zeng, Kevin Devincentis, Yao Xiao, Zubayer Ibne Ferdous, Xiaochen Guo, Zhiyuan Yan, Yevgeny Berdichevsky

Published: 2017-10-30T13:55:59.000Z

## Abstract

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 possibilities of using living neural networks in vitro as basic computational elements for machine learning applications. A new supervised STDP-based learning algorithm is proposed in this work, which considers neuron engineering constrains. A 74.7% accuracy is achieved on the MNIST benchmark for handwritten digit recognition.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1710.10944>)

[PDF](<https://arxiv.org/pdf/1710.10944.pdf>)