Index of papers in PLOS Comp. Biol. that mention

**cross-validated**

Detection of artificial EPSCs immersed in fluctuating noise | Using ROC analysis (1ms timescale, 10-fold cross-validated ), we find that spikes are predicted with an area-under-the-curve (AUC) of 0.71:0.01 for Model 1, while Model 2 yields 0.72:0.01, 0.74:0.01, and 0.77:0.01 as the input amplitude increases from 0.5 a, to 1.00 a, to 1.5 a. |

Detection of weight changes | As with the detection of connections, here we define a weight change as detectable if modeling the effects 191”ng and bpre¢>g around a known change-point t’ results in a cross-validated log likelihood ratio >0 when compared to a model with a single coupling effect bpre for all observations. |

Inferring functional connectivity from spikes | The regularization hyper-parameter is optimized by maximizing cross-validated log-likeli-hood, and no regularization is performed on the baseline or post-spike history terms. |

Prediction of spikes | Using ROC analysis we find that cross-validated area-under-the-curve increases from 0.72:0.01 when 5% of the inputs are observed to 0.98:0.01 in the model that includes all (100%) inputs (Fig. |

Quantifying accuracy and detecting functional connections | An important concept in functional connectivity analysis is whether or not an input is “de-tectable.” One approach is to define an input as detectable if the effect bpre, from the model in Eq.1, results in a cross-validated log likelihood ratio >0 when compared to the nested model with bpre = 0. |

Quantifying accuracy and detecting functional connections | Here we base “detection time” on the minimum amount of recorded data (averaged over blocks length T) needed to satisfy the cross-validated LLR criterion. |

Quantifying accuracy and detecting functional connections | In deciding whether a connection is present or not, it may be useful to compare the assumptions of the cross-validated log likelihood ratio and the un-cross-validated, explicit likelihood-ratio test. |

U | B) Model accuracy: Receiver operating characteristic (ROC) curves for the example cell and area under the curve (AUC) for all cells from A. Curves show the cross-validated false positive rate (FPR) vs true positive rate (TPR) for spike detection in 1ms bins. |

cross-validated is mentioned in 9 sentences in this paper.

Topics mentioned in this paper:

- synaptic inputs (32)
- functional connectivity (26)
- spike trains (19)

Introduction | We then performed a cross-validated evaluation to establish which of the four regularized estimators was most efficient for representing the population activity of dense groups of neurons in mouse primary visual corteX recorded with high-speed 3D random-access two-photon imaging of calcium signals. |

Model selection | Therefore, showing that a more constrained model has better cross-validated performance than a more compleX model does not necessarily support the conclusion that it reveals a better representation of dependencies in the data. |

an | To demonstrate that estimator rankings were robust to deviations from Gaussian models, we repeated the same cross-validated evaluation using pairwise Ising models to generate the data. |

an | This simulation study demonstrated that cross-validated evaluation of regularized estimators of the covariance matrices of population activity can discriminate between structures of dependencies in the population. |

cross-validated is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

Single-spike information and Poisson log-likelihood | This results in the “cross-validated” single-spike in-formation: |

Single-spike information and Poisson log-likelihood | Cross-validated single-spike information provides a useful measure for comparing models with different numbers of parameters (e.g., a l-filter vs. 2-filter LNP model), |

avg # of excitatory filters to (a ‘h at | (C) Cross-validated single-spike information for iSTAC, cbf-LNP, and rbf-LNP, as a function of the number of filters, averaged over a population of 16 neurons (selected from [29] for having 2 8 informative filters). |

cross-validated is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- log-likelihood (30)
- basis functions (20)
- mutual information (14)

PLS features support independent rather than synergy encoding in the DLMs | Reconstruction of decile averages (Fig 7C), cross-validated predictions (Fig 8D) and individual wing-strokes (Fig 9) are all improved by incorporating PLS features. |

Torque reconstruction improves with independently driven motor features | Finally, we cross-validated (D) the reconstructions performing 1000 replicates with 70% of the wingstrokes as a training set and 30% withheld for a test set. |

Torque waveform reconstruction | To test the predictive power of the reconstructions, we cross-validated the feature analysis using 70% of each decile of the data as a training set to predict the remaining 30%. |

cross-validated is mentioned in 3 sentences in this paper.

Topics mentioned in this paper: