Research PapersNo Access

Fitting of adaptive neuron model to electrophysiological recordings using particle swarm optimization algorithm

Bonan Shan

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P. R. China

Search for more papers by this author

Jiang Wang

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P. R. China

Search for more papers by this author

Lvxia Zhang

Yichang Testing Institute, Yichang 443000, P. R. China

E-mail Address: zhanglvxia@tju.edu.cn

Search for more papers by this author

Bin Deng

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P. R. China

E-mail Address: dengbin@tju.edu.cn

Corresponding author.

Search for more papers by this author

, and

Xile Wei

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P. R. China

Search for more papers by this author

https://doi.org/10.1142/S0217979217500230Cited by:4 (Source: Crossref)

Abstract

In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

Keywords:

PACS: 02.60.Pn, 05.45.Tp

You currently do not have access to the full text article.
Recommend the journal to your library today!

References

1. R. Brette and E. Guigon, Neural Comput. 15(2), 279 (2003). Web of Science, Google Scholar
2. Z. F. Mainen and T. J. Sejnowski, Science 268(5216), 1503 (1995). Web of Science, ADS, Google Scholar
3. A. V. M. Herz et al., Science 314(5796), 80 (2006). Web of Science, ADS, Google Scholar
4. A. L. Hodgkin and A. F. Huxley, J. Physiol. 117(4), 500 (1952). Web of Science, Google Scholar
5. R. FitzHugh, Biophys. J. 1(6), 445 (1961). Web of Science, Google Scholar
6. E. M. Izhikevich, IEEE Trans. Neural Netw. 14(6), 1569 (2003). Web of Science, Google Scholar
7. A. Cachón and R. A. Vázquez, Neurocomputing 148, 187 (2015). Web of Science, Google Scholar
8. S. Yamauchi, H. Kim and S. Shinomoto, Front. Comput. Neurosci. 5(14), 721 (2011). Google Scholar
9. L. Hertäg et al., Front. Comput. Neurosci. 6(1), 3310 (2012). Google Scholar
10. L. Badel et al., J. Neurophysiol. 99(2), 656 (2008). Web of Science, Google Scholar
11. C. Liu et al., Neurocomputing 151, 1507 (2015). Web of Science, Google Scholar
12. Q. Qin et al., Appl. Math. Model 39(3), 1400 (2015). Web of Science, Google Scholar
13. R. E. Kalman, J. Basic Eng. 82(1), 35 (1960). Google Scholar
14. S. J. Julier and J. K. Uhlmann, Proc. IEEE. 92(3), 401 (2004). Web of Science, Google Scholar
15. A. Mitra, A. Manitius and T. Sauer, in IEEE American Control Conf. (ACC). 346 (IEEE, 2013). Google Scholar
16. K. Deb, A. Pratap and T. A. M. T. Meyarivan, IEEE Trans. Evol. Comput. 6(2), 182 (2002). Web of Science, Google Scholar
17. R. Storn and K. Price, J. Glob. Optim. 11(4), 341 (1997). Web of Science, Google Scholar
18. J. Kennedy and R. Eberhart, in Proc. of IEEE International Conf. on Neural Networks 1942 (IEEE, 1995). Google Scholar
19. C. Rossant et al., Front. Neuroinform. 4, 2 (2010). Google Scholar
20. Y. Xu et al., Nonlinear Dyn. 81(4), 1717 (2015). Web of Science, Google Scholar
21. L. Pan et al., Control Conference (ECC). 1771 (2014). Google Scholar
22. L. Pan, Z. Guan and L. Zhou, Int. J. Bifurcat. Chaos. 23(08), 1350146 (2013). Link, Web of Science, Google Scholar
23. L. Pan, L. Zhou and D. Li, Nonlinear Dyn. 73(3), 2059 (2013). Web of Science, Google Scholar
24. C. Rossant et al., Front. Neurosci. 5, 9 (2011). Web of Science, Google Scholar
25. E. M. Izhikevich, IEEE Trans. Neural Netw. 15(5), 1063 (2004). Web of Science, Google Scholar
26. W. Gerstner and R. Naud, Science 326(5951), 379 (2009). Web of Science, Google Scholar
27. G. Fuhrmann, H. Markram and M. Tsodyks, J. Neurophysiol. 88(2), 761 (2002). Web of Science, Google Scholar
28. R. Kobayashi, Y. Tsubo and S. Shinomoto, Front. Comput. Neurosci. 3, 9 (2009). Web of Science, Google Scholar
29. S. Shinomoto, Neural Netw. 23(6), 764 (2010). Web of Science, Google Scholar
30. R. C. Eberhart and Y. Shi, Proceedings of the 2001 Congress on IEEE. 1, 81 (IEEE, 2001). Google Scholar
31. J. Kennedy et al., Swarm Intelligence (Morgan Kaufmann, 2001). Google Scholar
32. R. VanRullen, R. Guyonneau and S. J. Thorpe, Trends Neurosci. 28(1), 1 (2005). Web of Science, Google Scholar
33. J. Benda and A. V. M. Herz, Neural Comput. 15(11), 2523 (2003). Web of Science, Google Scholar
34. R. Jolivet, T. J. Lewis and W. Gerstner, J. Neurophysiol. 92(2), 959 (2004). Web of Science, Google Scholar
35. W. M. Kistler, W. Gerstner and J. L. van Hemmen, Neural Comput. 9(5), 1015 (1997). Web of Science, Google Scholar