Evolving Spiking Neural Networks for Nonlinear Control Problems

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Howard, David; Garratt, Matthew; Anavatti, Sreenatha; Qiu, Huanneng


2018-11-25


Conference Material


IEEE SSCI 2018 (Symposium Series on Computational Intelligence), Bengaluru India, 18th November 2018


1-6


Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons. Most of the research employing spiking neurons has been non-behavioural and discontinuous. Comparatively, this paper presents a recurrent spiking controller that is capable of solving nonlinear control problems in continuous domains using a popular topology evolution algorithm as the learning mechanism. We propose two mechanisms necessary to the decoding of continuous signals from discrete spike transmission: (i) a background current component to maintain frequency sufficiency for spike rate decoding, and (ii) a general network structure that derives strength from topology evolution. We demonstrate that the proposed spiking controller can learn significantly faster to discover functional solutions than sigmoidal neural networks in solving a classic nonlinear control problem.


IEEE


Aircraft Performance and Flight Control Systems


Link to Publisher's Version


Symposium Series on Computational Intelligence


EP187871


Conference Paper - Refereed


English


Howard, David; Garratt, Matthew; Anavatti, Sreenatha; Qiu, Huanneng. Evolving Spiking Neural Networks for Nonlinear Control Problems. In: IEEE SSCI 2018 (Symposium Series on Computational Intelligence); 18th November 2018; Bengaluru India. IEEE; 2018. 1-6. http://hdl.handle.net/102.100.100/86227?index=1



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