Some Graphical Methods for Assessing the Dependence Structure between Neuronal Spike Trains
Abstract
Suppose that two (possibly dependent) point processes are observed simultaneously over a period of time, yielding observations at times A1 < A2 ... < AN(A) for the first process, and at times B1 < B2 < ... BN(B) for the second. Such data arises in many contexts, and it is often of interest to discover and quantify the association between the two processes. Two fields in which this situation occurs are neurophysiology and reliability theory. In this article, we describe and discuss certain graphs, plots, as well as more formal methods that can assess the dependence between point processes. Specifically, these methods indicate whether or not the likelihood of an A-point is increased (decreased) after the occurrence of a B-point. The techniques are illustrated on simulated data. Although bivariate point processes arise in many fields, we emphasize the applications in neurophysiology. Keywords: Reliability theory.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 13, 1990
- Accession Number
- ADA220161
Entities
People
- Hani Doss
- Joseph Marhoul
Organizations
- Stanford University