Conditional Inference for Assessing the Statistical Significance 
		          of Neural Spiking Patterns
			       Matthew Harrison
			 Carnegie Mellon University

Conditional inference has proven useful for exploratory analysis of
neurophysiological point process data.  I will illustrate this approach and then
focus on a specific sub-problem: random generation of binary matrices with
margin constraints.  Sequential importance sampling (SIS) is an effective
technique for approximate uniform sampling of binary matrices with specified
margins.  I will describe how to simplify and improve existing SIS procedures
using improved asymptotic enumeration and dynamic programming (DP).  The DP
approach is interesting because it facilitates generalizations.