GP CaKe: Effective brain connectivity with causal kernels
Authors: Luca Ambrogioni, Max Hinne, Marcel Van Gerven, Eric Maris
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of GP Ca Ke on a number of simulations and give an example of a realistic application on magnetoencephalography (MEG) data. |
| Researcher Affiliation | Academia | Luca Ambrogioni Radboud University l.ambrogioni@donders.ru.nl Max Hinne Radboud University m.hinne@donders.ru.nl Marcel A. J. van Gerven Radboud University m.vangerven@donders.ru.nl Eric Maris Radboud University e.maris@donders.ru.nl |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific statement or link indicating that the source code for the methodology described in this paper is publicly available. |
| Open Datasets | No | The paper describes using 'magnetoencephalography (MEG) data' from a participant and 'separately generated training data' for simulations, but does not provide concrete access information (link, DOI, repository, or standard citation) for a publicly available dataset. |
| Dataset Splits | No | The paper mentions 'cross-validation on separately generated training data' but does not provide specific details on dataset splits (e.g., percentages or sample counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not specify the hardware used for running its experiments, such as specific GPU or CPU models, memory, or cloud resources. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Whenever a parameter is not specifically adjusted, we use the following default values: noise level σ = 0.05, temporal smoothing ν = 0.15 and temporal localization ϑ = π. Furthermore, we set ts = 0.05 throughout. |