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 Cybernjournal_title
Biological cyberneticsauthors
Chornoboy ES,Schramm LP,Karr AFdoi
10.1007/BF00332915subject
Has Abstractpub_date
1988-01-01 00:00:00pages
265-75issue
4-5eissn
0340-1200issn
1432-0770journal_volume
59pub_type
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