This structure is generated by the following equations: V^{(1)}_t = 0.2 * V^{(1)}_{t - 1} + 0.1 * \eps^1_t V^{(2)}_t = 0.2 * V^{(2)}_{t - 1} + 0.7 * V^{(1)}_{t - 1} + 0.1 * \eps^2_t V^{(3)}_t = 0.2 *

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Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

时序建模

贝叶斯行为优化

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests t

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Model architectures used for the experiments.

神经网络架构

时序建模

STPNet is the model with short-term synaptic adaptation and RNN is the recurrent neural network. Convolutional layers are denoted as “conv-”. “maxpool” denotes max pooling using a 2x2 window and a str

Figshare2021-09-17 更新30

Accounting for time series errors in partially linear model with single- or multiple-run

时序建模

磁共振影像分析

This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance

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Supplement 1. R code for simulation implementation and parameter estimation.

时序建模

参数估计方法比较

File List EstimationComparison.R (MD5: 5055e25ebc61fe8dbf012b48198eadda) Simulations.R (MD5: 56eef6b34cb59a85d5072b0b20e645ec) CreateFigures.R (MD5: 2c19a14d1ac5887ba544c2937d8639ca

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