Biological Cybernetics

, Volume 59, Issue 4–5, pp 265–275

Maximum likelihood identification of neural point process systems

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

Abstract

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.

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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
  3. 3.Lincoln LaboratoryMassachusetts Institute of TechnologyLexingtonUSA

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