Abstract
Liquid State Machines (LSMs) are a computational model of spiking neural networks with recurrent connections in a reservoir. Although they are believed to be biologically more plausible, LSMs have not yet been as successful as other artificial neural networks in solving real world learning problems mainly due to their highly sensitive learning performance to different types of stimuli. To address this issue, a covariance matrix adaptation evolution strategy has been adopted in this paper to optimize the topology and parameters of the LSM, thereby sparing the arduous task of fine tuning the parameters of the LSM for different tasks. The performance of the evolved LSM is demonstrated on three complex real-world pattern classification problems including image recognition and spatio-temporal classification.
This work was supported by the National Natural Science Foundation of China under Grand 61525302, 61590922, the National Key Research and Development Program of China under Grant 2018YFB1701104, the Project of Ministry of Industry and Information Technology of China under Grand 20171122-6, and the Fundamental Research Funds for the Central Universities under Grand N160801001, N161608001.
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Zhou, Y., Jin, Y., Ding, J. (2019). Evolutionary Optimization of Liquid State Machines for Robust Learning. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_41
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DOI: https://doi.org/10.1007/978-3-030-22796-8_41
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