Neural coding: Higher-order temporal patterns in the neurostatistics of cell assemblies

Recent advances in the technology of multiunit 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 approac hes to detecting the presence of spatiotemporal patterns of more than two n eurons in neuron spike train data. We mention three possible measures for t he presence of higher-order patterns of neural activation-coefficients of l og-linear models, connected cumulants, and redundancies-and present argumen ts in favor of the coefficients of log-linear models. We present test stati stics for detecting the presence of higher-order interactions in spike trai n data by parameterizing these interactions in terms of coefficients of log -linear models. We also present a Bayesian approach for inferring the exist ence or absence of interactions and estimating their strength. The two meth ods, the frequentist and the Bayesian one, are shown to be consistent in th e sense that interactions that are detected by either method also tend to b e detected by the other. A heuristic for the analysis of temporal patterns is also proposed. Finally, a Bayesian test is presented that establishes st ochastic differences between recorded segments of data. The methods are app lied to experimental data and synthetic data drawn from our statistical mod els. Our experimental data are drawn from multiunit recordings in the prefr ontal cortex of behaving monkeys, the somatosensory cortex of anesthetized rats, and multiunit recordings in the visual cortex of behaving monkeys.