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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.
Author Information
Mijung Park (UCL)
Gergo Bohner (Gatsby Unit, UCL)
Jakob H Macke (research center caesar & BCCN Tubingen)
More from the Same Authors
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2015 Poster: Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) »
Mijung Park · Wittawat Jitkrittum · Ahmad Qamar · Zoltan Szabo · Lars Buesing · Maneesh Sahani -
2014 Poster: Sparse Bayesian structure learning with dependent relevance determination prior »
Anqi Wu · Mijung Park · Sanmi Koyejo · Jonathan W Pillow