https://doi.org/10.1140/epjs/s11734-024-01162-x
Regular Article
Noise-induced alternations and data-driven parameter estimation of a stochastic perceptual model
1
School of Mathematics and Statistics, Shaanxi Normal University, 710119, Xi’an, China
2
School of Mathematics and Statistics, Northwestern Polytechnical University, 710072, Xi’an, China
3
MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, 710072, Xi’an, China
4
School of Science, Xi’an University of Posts and Telecommunications, 710121, Xi’an, China
5
Department of Systems and Control Engineering, Tokyo Institute of Technology, 152-8552, Tokyo, Japan
Received:
15
January
2024
Accepted:
1
April
2024
Published online:
15
April
2024
Neural systems are inherently noisy and our perceptual system can be then influenced from time to time. In this paper, we considered a perceptual model perturbed by Lévy colored noise, which is much easier to be satisfied in real-world environments than the general Gaussian noise. To elucidate the mechanism underlying the alternation behaviors induced by noise, we characterized the perceptual dynamics in terms of three statistical measures: the mean dominance duration, the number of alternations and the predominance of each interpretation. Numerical simulations showed that the stability index as well as the scale factor and the correlation time of the noise can lead to distinct changes in these measures. Then, attention was paid to data-driven parameter estimation which has typically received less attention than the exploration of stochastic behaviors. A distinctive neural network was proposed to give rise to joint estimates of system parameters and noise parameters, which can also give the measurement to describe the accuracy of estimation. The good performances of our method are shown by simulation tests.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.