Biological Cybernetics

, Volume 59, Issue 4–5, pp 265–275 | Cite as

Maximum likelihood identification of neural point process systems

  • E. S. Chornoboy
  • L. P. Schramm
  • A. F. Karr


Using the theory of random point processes, a method is presented whereby functional relationships between neurons can be detected and modeled. The method is based on a point process characterization involving stochastic intensities and an additive rate function model. Estimates are based on the maximum likelihood (ML) principle and asymptotic properties are examined in the absence of a stationarity assumption. An iterative algorithm that computes the ML estimates is presented. It is based on the expectation/maximization (EM) procedure of Dempster et al. (1977) and makes ML identification accessible to models requiring many parameters. Examples illustrating the use of the method are also presented. These examples are derived from simulations of simple neural systems that cannot be identified using correlation techniques. It is shown that the ML method correctly identifies each of these systems.


Rate Function Iterative Algorithm Point Process Functional Relationship Function Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 1988

Authors and Affiliations

  • E. S. Chornoboy
    • 1
  • L. P. Schramm
    • 1
  • A. F. Karr
    • 2
  1. 1.Department of Biomedical EngineeringThe Johns Hopkins School of MedicineBaltimoreUSA
  2. 2.Department of Mathematical Sciences, G.W.C. Whiting School of EngineeringThe Johns Hopkins UniversityBaltimoreUSA

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