A computationally efficient method for modeling neural spiking activity with point processes nonparametrically

Todd P. Coleman, Sridevi Sarma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Point process models have been shown to be useful in characterizing neural spiking activity (NSA) as a function of extrinsic and intrinsic factors. Most point process models of NSA are parametric as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but computation grows with the size of the data. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to be a Lipschitz continuous function but otherwise arbitrary. We show that by exploiting much structure, the problem becomes efficiently solvable and we compare our nonparametric estimation method to the most commonly used parametric approaches on goldfish retinal ganglion neural data. In this example, our nonparametric method gives a superior absolute goodness-of-fit measure than all parametric approaches analyzed.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5800-5805
Number of pages6
ISBN (Print)1424414989, 9781424414987
DOIs
StatePublished - 2007
Externally publishedYes
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
Country/TerritoryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint

Dive into the research topics of 'A computationally efficient method for modeling neural spiking activity with point processes nonparametrically'. Together they form a unique fingerprint.

Cite this