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Stochastic Hyperkernel Convolution Trains and h-Counting Processes
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DOI：10.1109/ACCESS.2023.3246385.png)
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摘要
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The paper presents two new families of stochastic processes called hyperkernel convolution train and h-counting processes. These models generalize respectively the spike train and counting process models. The convolution train model is designed to encompass both continuous and singular spiking activities. The h-counting process can be used to model counting phenomena for which the increments are not necessarily instantaneous. This h-counting model can also be used to represent uncertainties on the exact locations of state transitions of a standard discrete event system. The paper also highlights some statistical properties of the provided convolution train model, in addition to a framework based on wavelet packets for simulating or learning such a process from multiple observations of disturbed input trains.
Keyword:
Convolutional neural networks
Stochastic processes
Kernel
Computational modeling
Analytical models
Wavelet packets
Standards
Convolution train
h-counting process
spike train
counting process
hyperkenel train
wavelet train
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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IEEE ACCESS
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IEEE ACCESSIF3.6
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