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Tuesday, December 8 • 19:00 - 23:59
Unlocking neural population non-stationarities using hierarchical dynamics models

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Neural population activity often exhibits rich variability. This variability is thought to arise from single-neuron stochasticity, neural dynamics 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 introduce a hierarchical dynamics model that is able to capture inter-trial modulations in firing rates, as well as neural population dynamics. We derive an algorithm for Bayesian Laplace propagation for fast posterior inference, and demonstrate that our model provides a better account of the structure of neural firing than existing stationary dynamics models, when applied to neural population recordings from primary visual cortex.

Tuesday December 8, 2015 19:00 - 23:59
210 C #21

Attendees (2)