Maximum likelihood identification of neural point process systems.

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.

journal_name

Biol Cybern

journal_title

Biological cybernetics

authors

Chornoboy ES,Schramm LP,Karr AF

doi

10.1007/BF00332915

subject

Has Abstract

pub_date

1988-01-01 00:00:00

pages

265-75

issue

4-5

eissn

0340-1200

issn

1432-0770

journal_volume

59

pub_type

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