Corpus ID: 222291079

Point process models for sequence detection in high-dimensional neural spike trains

@article{Williams2020PointPM,
  title={Point process models for sequence detection in high-dimensional neural spike trains},
  author={Alex H. Williams and Anthony Degleris and Yixin Wang and Scott W. Linderman},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.04875}
}
  • Alex H. Williams, Anthony Degleris, +1 author Scott W. Linderman
  • Published 2020
  • Computer Science, Mathematics, Biology
  • ArXiv
  • Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience. Promising recent work utilized a convolutive nonnegative matrix factorization model to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model… CONTINUE READING

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