Titlebook: Computational Neuroscience; 4th Latin American W Jaime A. Riascos Salas,Vinícius Rosa Cota,Daniel B Conference proceedings 2024 The Editor(

Acclaim

https://doi.org/10.1007/978-3-662-39645-2are results to the ones obtained using an algorithm that is commonly used in the literature: the SVM. Our results indicated that the proposed ECG-TSC significantly outperformed an ECG-SVM classifier (a commonly used algorithm in the literature) when using 6, 10, 20, 30 and 40-s time windows, suggest

THROB

https://doi.org/10.1007/978-3-662-39645-2previously reported synaptic plasticity computational model developed by Dainauskas et al. (2023) [.]. For the Wnt/.-Catenin pathway model, we implemented differential equations in Python to analyze five key elements of the pathway in the context of AD: Wnt, GSK3-., .-Catenin, BACE1 gene, and Amyloi

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一骂死割除

https://doi.org/10.1007/978-94-6300-920-1gainst a rest-state. The results show that EEGNeX outperforms EEGNet, improving classification by 4–9% depending on the OA removal technique used. While the OA removal techniques do not significantly differ, EEGNeX performs best with accuracies of over 71% for RGB classification and over 85% for Col