Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

Recent advances in the technology of multi-unit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron spike train data. We examine three measures for the presence of higher order patterns of neural activation -- coefficients of log-linear models, connected cumulants, and redundancies -- and present arguments in favor of the coefficients of log-linear models. We present test statistics for detecting the presence of higher order interactions in spike train data. We also present a Bayesian approach for inferring the existence or absence of interactions and estimating their strength. The two methods are shown to be consistent in the sense that highly significant correlations are also highly probable. A heuristic for the analysis of temporal patterns is also proposed. The ...

• Type: Journal
• Year: 2000
• Where: NECO
• Authors: Laura Martignon, Gustavo Deco, Kathryn B. Laskey, Mathew E. Diamond, Winrich Freiwald, Eilon Vaadia
• Keywords: Log-linear Models, Multi-unit Recordings, NECO 2000, Neuron