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


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.


  1. Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nat Neurosci 3:1178–1183

    CAS  Article  Google Scholar 

  2. Becraft WR, Lee PL, Newell RB (1991) Integration of neural networks and expert systems for process fault diagnosis. In: 12th International Joint Conference on Artificial Intelligence, Sydney, Australia

  3. Broner I, Comstock CR (1997) Combining expert systems and neural networks for learning site-specific conditions. Comput Electron Agr 19:37–53

    Article  Google Scholar 

  4. Chung J, Gulcehre C, Cho KH, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv arXiv:1412.3555

  5. Di Maio V, Santillo S, Sorgente A, Vanacore P, Ventriglia F (2018) Influence of active synaptic pools on the single synaptic event. Cogn Neurodyn 12:391–402

    Article  Google Scholar 

  6. Fu J, Wang H, Gao J, Yu M, Wang R, Yang Z, Zhang T (2017) Rapamycin effectively impedes melamine-induced impairments of cognition and synaptic plasticity in wistar rats. Mol Neurobiol 54:819–832

    CAS  Article  Google Scholar 

  7. Gammulle H, Denman S, Sridharan S, Fookes C (2017) Two Stream LSTM: A deep fusion framework for human action recognition. Eprint Arxiv arXiv:1704.01194

  8. Herrmann CS, Arnold T, Visbeck A, Hundemer HP, Hopf HC (2001) Adaptive frequency decomposition of EEG with subsequent expert system analysis. Comput Biol Med 31:407–427

    CAS  Article  Google Scholar 

  9. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    CAS  Article  Google Scholar 

  10. Huang Y, Yang S, Hu ZY, Liu G, Zhou WX, Zhang YX (2012) A new approach to location of the dentate gyrus and perforant path in rats/mice by landmarks on the skull. Acta Neurobiol Exp (Wars) 72:468–472

    Google Scholar 

  11. Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36:3465–3469

    Article  Google Scholar 

  12. Kim S-Y, Lim W (2019) Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity. Cogn Neurodyn 13:53–73

    Article  Google Scholar 

  13. Kim S-Y, Lim W (2020) Effect of interpopulation spike-timing-dependent plasticity on synchronized rhythms in neuronal networks with inhibitory and excitatory populations. Cogn Neurodyn.

    Article  PubMed  Google Scholar 

  14. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

    Google Scholar 

  15. Kramar EA, Lin B, Lin CY, Arai AC, Gall CM, Lynch G (2004) A novel mechanism for the facilitation of theta-induced long-term potentiation by brain-derived neurotrophic factor. J Neurosci 24:5151–5161

    CAS  Article  Google Scholar 

  16. Kroger JK (1989) The applicability and limitations of expert system shells. vol 89-32

  17. Liu YC, Chang CC, Yang YS, Liang C (2018) Spontaneous analogising caused by text stimuli in design thinking: differences between higher- and lower-creativity groups. Cogn Neurodyn 12:55–71

    Article  Google Scholar 

  18. Melin P, Soto J, Castillo O, Soria J (2012) A new approach for time series prediction using ensembles of ANFIS models. Expert Syst Appl 39:3494–3506

    Article  Google Scholar 

  19. Nikolov V, Bogdanov V (2010) Integration of neural networks and expert systems for time series prediction. In: 11th International Conference on Computer Systems and Technologies and Workshop, Sofia, Bulgaria

  20. Peter J (1998) Introduction to expert systems, 3rd edn. Addison Wesley, Hoboken

    Google Scholar 

  21. Pfefferkorn C, Burr D, Harrison D, Heckman B, Oresky C, Rothermel J (1985) ACES: A cartographic Expert System. Proceedings of the Auto-Carto 7

  22. Salinas D, Flunkert V, Gasthaus J (2017) DeepAR: probabilistic forecasting with autoregressive recurrent networks. Eprint Arxiv arXiv:1704.04110

  23. Shang Y, Wang X, Shang X, Zhang H, Liu Z, Yin T, Zhang T (2016) Repetitive transcranial magnetic stimulation effectively facilitates spatial cognition and synaptic plasticity associated with increasing the levels of BDNF and synaptic proteins in Wistar rats. Neurobiol Learn Mem 134:369–378

    CAS  Article  Google Scholar 

  24. Shang X, Shang Y, Fu J, Zhang T (2017) Nicotine significantly improves chronic stress-induced impairments of cognition and synaptic plasticity in mice. Mol Neurobiol 54:4644–4658

    CAS  Article  Google Scholar 

  25. Shang Y, Wang X, Li F, Yin T, Zhang J, Zhang T (2019) rTMS ameliorates prenatal stress-induced cognitive deficits in male-offspring rats associated with BDNF/TrkB signaling pathway. Neurorehab Neural Re 33:271–283

    Article  Google Scholar 

  26. Šíma J (1995) Neural expert systems. Neural Netw 8(2):261–271

    Article  Google Scholar 

  27. Tamura R, Eifuku S, Uwano T, Sugimori M, Uchiyama K, Ono T (2011) A method for recording evoked local field potentials in the primate dentate gyrus in vivo. Hippocampus 21:565–574

    Article  Google Scholar 

  28. Vargas JY, Fuenzalida M, Inestrosa NC (2014) In vivo activation of Wnt signaling pathway enhances cognitive function of adult mice and reverses cognitive deficits in an Alzheimer’s disease model. J Neurosci 34:2191–2202

    CAS  Article  Google Scholar 

  29. Venkitaramani DV, Chin J, Netzer WJ, Gouras GK, Lesne S, Malinow R, Lombroso PJ (2007) Beta-amyloid modulation of synaptic transmission and plasticity. J Neurosci 27:11832–11837

    CAS  Article  Google Scholar 

  30. Wu S, Zhou K, Ai Y, Zhou G, Yao D, Guo D (2020) Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity. Cogn Neurodyn.

    Article  Google Scholar 

  31. Xiang S, Zhou Y, Fu J, Zhang T (2019) rTMS pre-treatment effectively protects against cognitive and synaptic plasticity impairments induced by simulated microgravity in mice. Behav Brain Res 359:639–647

    Article  Google Scholar 

  32. Yu M, Zhang Y, Chen X, Zhang T (2016) Antidepressant-like effects and possible mechanisms of amantadine on cognitive and synaptic deficits in a rat model of chronic stress. Stress 19:104–113

    CAS  Article  Google Scholar 

  33. Zhang S, Maeda J (2000) A rule-based expert system for automatic segmentation of cerebral MRI images. Science Journal of Kanagawa University 18:2133–2138

    Google Scholar 

  34. Zhang M, Zheng C, Quan M, An L, Zhang T (2011) Directional indicator on neural oscillations as a measure of synaptic plasticity in chronic unpredictable stress rats. Neurosignals 19:189–197

    CAS  Article  Google Scholar 

  35. Zhang H, Shang Y, Xiao X, Yu M, Zhang T (2017) Prenatal stress-induced impairments of cognitive flexibility and bidirectional synaptic plasticity are possibly associated with autophagy in adolescent male-offspring. Exp Neurol 298:68–78

    Article  Google Scholar 

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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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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).

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