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pdf:docinfo:title: Inferring neural population dynamics from multiple partial recordings of the same neural circuit
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EventType: Spotlight
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subject: Neural Information Processing Systems http://nips.cc/
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dc:title: Inferring neural population dynamics from multiple partial recordings of the same neural circuit
Book: Advances in Neural Information Processing Systems 26
pdf:docinfo:custom:Date: 2013
Description-Abstract: Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons  using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model  by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs."
cp:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:custom:Created: 2013
pdf:docinfo:creator: Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
meta:author: Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
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lastpage: 547
pdf:docinfo:custom:Type: Conference Proceedings
Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger
Author: Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
producer: Python PDF Library - http://pybrary.net/pyPdf/
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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: 539
dc:creator: Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
pdf:docinfo:custom:EventType: Spotlight
title: Inferring neural population dynamics from multiple partial recordings of the same neural circuit
Created: 2013
Language: en-US
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pdf:docinfo:custom:Book: Advances in Neural Information Processing Systems 26
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creator: Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
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Type: Conference Proceedings
pdf:docinfo:custom:Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger
pdf:docinfo:custom:Description-Abstract: Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons  using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model  by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs."
pdf:docinfo:custom:Publisher: Curran Associates
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