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  A Bayesian model for identifying hierarchically organised states in neural population activity

Putzky, P., Franzen, F., Bassetto, G., & Macke, J. (2014). A Bayesian model for identifying hierarchically organised states in neural population activity. In Ghahramani, Z., M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 3095-3103).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-807F-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FDC5-5
Genre: Conference Paper

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 Creators:
Putzky, P1, 2, Author
Franzen, F1, 2, Author
Bassetto, G1, 2, Author
Macke, JH1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states. These cortical states vary on multiple time-scales and also across areas and layers of the neocortex. To understand information processing in cortical circuits, we need to understand the statistical structure of internal states and their interaction with sensory inputs. Here, we present a statistical model for extracting hierarchically organized neural population states from multi-channel recordings of neural spiking activity. We model population states using a hidden Markov decision tree with state-dependent tuning parameters and a generalized linear observation model. Using variational Bayesian inference, we estimate the posterior distribution over parameters from population recordings of neural spike trains. On simulated data, we show that we can identify the underlying sequence of population states over time and reconstruct the ground truth parameters. Using extracellular population recordings from visual cortex, we find that a model with two levels of population states outperforms a generalized linear model which does not include state-dependence, as well as models which only including a binary state. Finally, modelling of state-dependence via our model also improves the accuracy with which sensory stimuli can be decoded from the population response.

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Language(s):
 Dates: 2014-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: PutzkyFBM2014
 Degree: -

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Title: Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)
Place of Event: Montréal, Quebec, Canada
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Title: Advances in Neural Information Processing Systems 27
Source Genre: Proceedings
 Creator(s):
Ghahramani, Editor
Z., Editor
Welling, M., Editor
Cortes, C., Editor
Lawrence, N. D., Editor
Weinberger, K. Q., Editor
Affiliations:
-
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3095 - 3103 Identifier: -