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  Unlocking neural population non-stationarity using a hierarchical dynamics model

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 (Eds.), Advances in Neural Information Processing Systems 28 (pp. 145-153). Red Hook, NY, USA: Curran.

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 Creators:
Park, M, Author              
Bohner, G, Author
Macke, J1, 2, 3, Author              
Affiliations:
1Former Research Group Neural Computation and Behaviour, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528699              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Center of Advanced European Studies and Research (caesar), Max Planck Society, ou_2173675              

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 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.

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 Dates: 2016
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: ParkBM2015
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Title: Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015)
Place of Event: Montréal, Canada
Start-/End Date: 2015-12-07 - 2015-12-12

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Title: Advances in Neural Information Processing Systems 28
Source Genre: Proceedings
 Creator(s):
Cortes, C, Editor
Lawrence, ND, Editor
Lee, DD, Editor
Sugiyama, M, Editor
Garnett, R, Editor
Affiliations:
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Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 145 - 153 Identifier: -