High-dimensional neural spike train analysis with generalized count linear dynamical systems

@inproceedings{Gao2015HighdimensionalNS,
  title={High-dimensional neural spike train analysis with generalized count linear dynamical systems},
  author={Yuanjun Gao and Lars Buesing and Krishna V. Shenoy and John P. Cunningham},
  booktitle={NIPS},
  year={2015}
}
Latent factor models have been widely used to analyze simultaneous recordings of spike trains from large, heterogeneous neural populations. These models assume the signal of interest in the population is a low-dimensional latent intensity that evolves over time, which is observed in high dimension via noisy point-process observations. These techniques have been well used to capture neural correlations across a population and to provide a smooth, denoised, and concise representation of high… CONTINUE READING

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