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

Putzky, P., Franzen, F., Bassetto, G., & Macke, J. (2015). A Bayesian model for identifying hierarchically organised states in neural population activity. In Z., Ghahramani, M., Welling, C., Cortes, N. D., Lawrence, & K. Q., Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 3095-3103). Red Hook, NY, USA: Curran.

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アイテムのパーマリンク: https://hdl.handle.net/11858/00-001M-0000-002A-47B5-B 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0000-FAC4-9
資料種別: 会議論文

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 作成者:
Putzky, P1, 著者           
Franzen, F1, 著者           
Bassetto, G1, 2, 著者           
Macke, JH3, 著者           
所属:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
3Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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キーワード: -
 要旨: 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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 日付: 2015
 出版の状態: 出版
 ページ: -
 出版情報: -
 目次: -
 査読: -
 識別子（DOI, ISBNなど）: BibTex参照ID: PutzkyFBM2014
 学位: -

関連イベント

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イベント名: Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)
開催地: Montréal, Quebec, Canada
開始日・終了日: -

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出版物 1

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出版物名: Advances in Neural Information Processing Systems 27
種別: 会議論文集
 著者・編者:
Ghahramani, Z., 編集者
Welling, M., 編集者
Cortes, C., 編集者
Lawrence, N. D., 編集者
Weinberger, K. Q., 編集者
所属:
-
出版社, 出版地: Red Hook, NY, USA : Curran
ページ: - 巻号: - 通巻号: - 開始・終了ページ: 3095 - 3103 識別子（ISBN, ISSN, DOIなど）: ISBN: 978-1-5108-0041-0