Abstract
A PID controller is a simple and general-purpose way of providing responsive control of dynamic systems with reduced overshoot and oscillation. Spiking neural networks offer some advantages for dynamic systems control, including an ability to adapt, but it is not obvious how to alter such a control network’s parameters to shape its response curve. In this paper we present a spiking neural PID controller: a small network of neurons that mimics a PID controller by using the membrane recovery variable in Izhikevich’s simple model of spiking neurons to approximate derivative and integral functions.
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References
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, 1st edn. The MIT Press (2001)
Furber, S., Temple, S.: Neural systems engineering. Journal of The Royal Society Interface 4(13), 193–206 (2007)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models, 1st edn. Cambridge University Press (2002)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 117(4), 500–544 (1952)
Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2003)
Izhikevich, E.M.: Which Model to Use for Cortical Spiking Neurons?. IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004)
Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (Computational Neuroscience), 1st edn. The MIT Press (2006)
Jin, X., Furber, S.B., Woods, J.V.: Efficient modelling of spiking neural networks on a scalable chip multiprocessor, pp. 2812–2819 (June 2008)
Rast, A.D., Khan, M.M., Jin, X., Plana, L.A., Furber, S.B.: A universal abstract-time platform for real-time neural networks. In: Proceedings of the 2009 International Joint Conference on Neural Networks, IJCNN 2009, pp. 3378–3385. IEEE Press, Piscataway (2009)
Rast, A.D., Jin, X., Galluppi, F., Plana, L.A., Patterson, C., Furber, S.: Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system. In: Proceedings of the 7th ACM International Conference on Computing Frontiers, CF 2010, pp. 21–30. ACM, New York (2010)
Shu, H., Pi, Y.: Pid neural networks for time-delay systems. Computers & Chemical Engineering 24(2-7), 859 (2000)
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Webb, A., Davies, S., Lester, D. (2011). Spiking Neural PID Controllers. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_28
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DOI: https://doi.org/10.1007/978-3-642-24965-5_28
Publisher Name: Springer, Berlin, Heidelberg
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