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  Neural system identification for large populations separating "what" and "where"

Klindt, D., Ecker, A., Euler, T., & Bethge, M. (2018). Neural system identification for large populations separating "what" and "where". In I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, et al. (Eds.), Advances in Neural Information Processing Systems 30 (pp. 3507-3517). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-C369-E Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B66E-7
Genre: Conference Paper

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 Creators:
Klindt, D, Author
Ecker, A, Author              
Euler, T, Author
Bethge, M1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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 Abstract: Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional neural system identification methods do not capitalize on this separation of "what" and "where". Learning deep convolutional feature spaces shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations -- a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse pooling layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We explore this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms the current state-of-the art system identification model of mouse primary visual cortex on a publicly available dataset.

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 Dates: 2018-06
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: BibTex Citekey: KlindtEEB2017_2
 Degree: -

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Title: Thirty-First Conference on Neural Information Processing Systems (NIPS 2017)
Place of Event: Long Beach, CA, USA
Start-/End Date: 2017-12-04 - 2017-12-09

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Title: Advances in Neural Information Processing Systems 30
Source Genre: Proceedings
 Creator(s):
Guyon, I, Editor
von Luxburg, U, Editor
Bengio, S, Editor
Wallach, HM, Editor
Fergus, R, Editor
Vishwanathan , SVN, Editor
Garnett, R, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3507 - 3517 Identifier: ISBN: 978-1-5108-6096-4