Zhao Shaokai, Shang Yingchun, Yang Ze, Xiao Xi, Zhang Jianhai, Zhang Tao
College of Life Sciences and Key Laboratory of Bioactive Materials Ministry of Education, Nankai University, Tianjin, 300071 People's Republic of China.
School of Computer Science & Technology, and Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, 310018 People's Republic of China.
Cogn Neurodyn. 2021 Apr;15(2):253-263. doi: 10.1007/s11571-020-09610-9. Epub 2020 Jun 29.
The indexes of synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), can usually be measured by evaluating the slope and/or magnitude of field excitatory postsynaptic potentials (fEPSPs). So far, the process depends on manually labeling the linear portion of fEPSPs one by one, which is not only a subjective procedure but also a time-consuming job. In the present study, a novel approach has been developed in order to objectively and effectively evaluate the index of synaptic plasticity. Firstly, we introduced an expert system applying symbolic rules to discard the contaminated waveform in an interpretable way, and further generate supervisory signals for subsequent seq 2seq model based on neural networks. For the propose of enhancing the system generalization ability to deal with the contaminated data of fEPSPs, we employed long short-term memory (LSTM) networks. Finally, the comparison was performed between the automatically labeling system and manually labeling system. These results show that the expert system achieves an accuracy of 96.22% on Type-I labels, and the LSTM supervised by the expert system obtains an accuracy of 96.73% on Type-II labels. Compared to the manually labeling system, the hybrids system is able to measure the index of synaptic plasticity more objectively and efficiently. The new system can reach the level of the human expert ability, and accurately produce the index of synaptic plasticity in a fast way.
突触可塑性指标，包括长时程增强（LTP）和长时程抑制（LTD），通常可通过评估场兴奋性突触后电位（fEPSP）的斜率和/或幅度来测量。到目前为止，该过程依赖于逐一手动标记fEPSP的线性部分，这不仅是一个主观过程，而且是一项耗时的工作。在本研究中，开发了一种新方法，以便客观有效地评估突触可塑性指标。首先，我们引入了一个应用符号规则的专家系统，以可解释的方式丢弃受污染的波形，并基于神经网络为后续的序列到序列（seq2seq）模型生成监督信号。为了提高系统处理fEPSP污染数据的泛化能力，我们采用了长短期记忆（LSTM）网络。最后，对自动标记系统和手动标记系统进行了比较。这些结果表明，专家系统在I型标签上的准确率达到96.22%，由专家系统监督的LSTM在II型标签上的准确率达到96.73%。与手动标记系统相比，混合系统能够更客观有效地测量突触可塑性指标。新系统可以达到人类专家能力的水平，并能快速准确地生成突触可塑性指标。
