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Unlocking neural population non-stationarity using a hierarchical dynamics model

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Park,  M
Former Research Group Neural Computation and Behaviour, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Macke,  J
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Park, M., Bohner, G., & Macke, J. (2016). Unlocking neural population non-stationarity using a hierarchical dynamics model. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (pp. 145-153). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7AB8-8
Abstract
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.