Application of expert system and LSTM in extracting index of synaptic plasticity

Abstract

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.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author (TZ) upon reasonable request.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (31771148, 61633010), Key Research & Development Project of Zhejiang Province (2020C04009), and 111 Project (B08011).

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Contributions

TZ, JZ: Conceived and designed the experiment, SZ, YCS, XX: Performed the experiments, SZ, ZY: Analyzed the data, SZ, TZ: Wrote the manuscript.

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Correspondence to Jianhai Zhang or Tao Zhang.

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Zhao, S., Shang, Y., Yang, Z. et al. Application of expert system and LSTM in extracting index of synaptic plasticity. Cogn Neurodyn (2020). https://doi.org/10.1007/s11571-020-09610-9

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Keywords

  • Expert system
  • Long short-term memory
  • Synaptic plasticity
  • LTP