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Abstract:
Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynam-
ics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as “non-stationarity”. To better understand the
nature of co-variability in neural circuits and their impact on cortical information processing, we need statistical models that are able to capture multiple sources
of variability on different time-scales. Here, we introduce a hierarchical statistical model of neural population activity which models both neural population dynamics as well as inter-trial modulations in firing rates. In addition, we extend the model to allow us to capture non-stationarities in the population dynamics itself (i.e., correlations across neurons). We develop variational inference methods for learning model parameters, and demonstrate that the method can recover
non-stationarities in both average firing rates and correlation structure. Applied to neural population recordings from anesthetized macaque primary visual cortex,
our models provide a better account of the structure of neural firing than stationary dynamics models.
