date: 2014-12-02T23:45:01Z
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pdf:docinfo:title: A Bayesian model for identifying hierarchically organised states in neural population activity
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subject: Neural Information Processing Systems http://nips.cc/
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dc:title: A Bayesian model for identifying hierarchically organised states in neural population activity
Book: Advances in Neural Information Processing Systems 27
pdf:docinfo:custom:Date: 2014
modified: 2014-12-02T23:45:01Z
Description-Abstract: Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states which vary on multiple time-scales. 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 organised neural population states from multi-channel recordings of neural spiking activity. Population states are modelled using a hidden Markov decision tree with state-dependent tuning parameters and a generalised linear observation model. We present a variational Bayesian inference algorithm for estimating the posterior distribution over parameters from neural population recordings. On simulated data, we show that we can identify the underlying sequence of population states and reconstruct the ground truth parameters. Using population recordings from visual cortex, we find that a model with two levels of population states outperforms both a one-state and a two-state generalised linear model. Finally, we find that modelling of state-dependence also improves the accuracy with which sensory stimuli can be decoded from the population response.
cp:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:custom:Created: 2014
pdf:docinfo:creator: Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
meta:author: Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
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lastpage: 3103
pdf:docinfo:custom:Type: Conference Proceedings
Editors: Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger
Author: Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
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pdf:docinfo:producer: PyPDF2
pdf:docinfo:custom:Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/)
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Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/)
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firstpage: 3095
dc:creator: Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
pdf:docinfo:custom:EventType: Spotlight
Last-Modified: 2014-12-02T23:45:01Z
dcterms:modified: 2014-12-02T23:45:01Z
title: A Bayesian model for identifying hierarchically organised states in neural population activity
Last-Save-Date: 2014-12-02T23:45:01Z
Created: 2014
pdf:docinfo:modified: 2014-12-02T23:45:01Z
Language: en-US
pdf:docinfo:custom:Language: en-US
pdf:docinfo:custom:Book: Advances in Neural Information Processing Systems 27
meta:save-date: 2014-12-02T23:45:01Z
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creator: Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
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xmpTPg:NPages: 9
Publisher: Curran Associates, Inc.
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Date: 2014
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Type: Conference Proceedings
pdf:docinfo:custom:Editors: Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger
pdf:docinfo:custom:Description-Abstract: Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states which vary on multiple time-scales. 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 organised neural population states from multi-channel recordings of neural spiking activity. Population states are modelled using a hidden Markov decision tree with state-dependent tuning parameters and a generalised linear observation model. We present a variational Bayesian inference algorithm for estimating the posterior distribution over parameters from neural population recordings. On simulated data, we show that we can identify the underlying sequence of population states and reconstruct the ground truth parameters. Using population recordings from visual cortex, we find that a model with two levels of population states outperforms both a one-state and a two-state generalised linear model. Finally, we find that modelling of state-dependence also improves the accuracy with which sensory stimuli can be decoded from the population response.
pdf:docinfo:custom:Published: 2014
Published: 2014
pdf:docinfo:custom:Publisher: Curran Associates, Inc.
access_permission:can_modify: true