Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions

@article{Song2013IdentificationOS,
  title={Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions},
  author={Dong Song and Haonan Wang and Catherine Y. Tu and Vasilis Z. Marmarelis and Robert E. Hampson and Sam A. Deadwyler and Theodore W. Berger},
  journal={Journal of Computational Neuroscience},
  year={2013},
  volume={35},
  pages={335-357}
}
One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the… CONTINUE READING
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Functional model selection for sparse binary time series with multiple inputs

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