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  Inferring neural population dynamics from multiple partial recordings of the same neural circuit

Turaga, S., Buesing, L., Packer, A., Dalgleish, H., Pettit, N., Hausser, M., et al. (2014). Inferring neural population dynamics from multiple partial recordings of the same neural circuit. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 26 (pp. 539-547). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0027-80BF-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-2D53-0
Genre: Conference Paper

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 Creators:
Turaga, SC, Author
Buesing, L, Author
Packer, AM, Author
Dalgleish, H, Author
Pettit, N, Author
Hausser, M, 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: 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.

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 Dates: 2013-122014
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: TuragaBPDPHM2013
 Degree: -

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Title: Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS 2013)
Place of Event: Stateline, NV, USA
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems 26
Source Genre: Proceedings
 Creator(s):
Burges, CJC, Editor
Bottou, L., Editor
Welling, M., Editor
Ghahramani, Z., Editor
Weinberger, K.Q., Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 539 - 547 Identifier: ISBN: 978-1-63266-024-4