Estimating a State-Space Model from Point Process Observations

  title={Estimating a State-Space Model from Point Process Observations},
  author={Anne C. Smith and Emery N. Brown},
  journal={Neural Computation},
A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process… CONTINUE READING
Highly Influential
This paper has highly influenced 37 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 585 citations. REVIEW CITATIONS
198 Extracted Citations
43 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 198 extracted citations

585 Citations

Citations per Year
Semantic Scholar estimates that this publication has 585 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 43 references

Time series analysis of non-gaussian observations based on state space models from both classical and Bayesian perspectives

  • J. Durbin, S. J. Koopman
  • J. Roy. Statist. Soc. B,
  • 2000
Highly Influential
2 Excerpts

Unobserved Monte-Carlo method for identification of partiallyobserved nonlinear state-space systems, part II: counting process observations

  • V. Solo
  • In Proc. IEEE Conference on Decision and Control
  • 2000
Highly Influential
6 Excerpts

Monte Carlo estimation for time series models involving counts

  • K. S. Chan, J. Ledolter
  • J. Am. Stat. Assoc.,
  • 1995
Highly Influential
1 Excerpt

Random point processes in time and space

  • D. L. Snyder, M. I. Miller
  • 1991
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…