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@article{Smith2003EstimatingAS,
  title={Estimating a State-Space Model from Point Process Observations},
  author={Anne C. Smith and Emery N. Brown},
  journal={Neural Computation},
  year={2003},
  volume={15},
  pages={965-991},
  url={https://api.semanticscholar.org/CorpusID:10020032}
}

Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, an algorithm to estimate a state-space model observed through point process measurements is developed.

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Dynamic Analyses of Information Encoding in Neural Ensembles

A general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal is presented and an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted is suggested.