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  Hierarchical models for neural population dynamics in the presence of non-stationarity

Park, M., & Macke, J. (2015). Hierarchical models for neural population dynamics in the presence of non-stationarity. -, submitted.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-479D-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FA96-D
Genre: Journal Article

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http://arxiv.org/pdf/1410.3111v1 (Any fulltext)
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 Creators:
Park, M, Author
Macke, JH1, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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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.

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 Dates: 2015-01
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: ParkM2014
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Source Genre: Journal
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