Details

Autor(en) / Beteiligte

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

Titel

A Supervised Stdp-Based Training Algorithm for Living Neural Networks

Ist Teil von

• 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, p.1154-1158

Ort / Verlag

IEEE

Erscheinungsjahr

2018

Quelle

IEL

Beschreibungen/Notizen

• 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.