Abstract
Attempts have been made to realize reservoir computing by using physical materials, but they assume the stable structure of a reservoir. However, in reality, a physical reservoir suffers from malfunctions, noise, and interferences, which cause failures of neurons and disconnection of synaptic connections. Consequently dynamics of system state changes and computation performance deteriorates. In this paper, we investigate structural properties contributing to the functional robustness of a reservoir. More specifically, we analyze the relationship between structural properties of a reservoir of a Liquid State Machine and the decrease in discrimination capability in a delayed readout task when experiencing failures of connections and neurons. We apply seven types of networks which have different structural properties to a reservoir. As a result, we revealed that high modularity, structural irregularity, and high clustering coefficient are most important for an LSM to be robust against random connection and neuron failures.
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Acknowledgements
This study was partly supported by JSPS KAKENHI Grant Number 16H01719. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
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Okumura, Y., Wakamiya, N. (2020). Analysis of Reservoir Structure Contributing to Robustness Against Structural Failure of Liquid State Machine. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_35
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DOI: https://doi.org/10.1007/978-3-030-61616-8_35
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