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  Neural system identification for large populations separating “what” and “where”

Klindt, D., Ecker, A., Euler, T., & Bethge, M. (2017). Neural system identification for large populations separating “what” and “where”. Poster presented at Bernstein Conference 2017, Berlin, Germany. doi:10.12751/nncn.bc2017.0132.

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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 current state-of-the art system identification models in the mouse visual system.

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 Dates: 2017-09
 Publication Status: Published online
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 Identifiers: DOI: 10.12751/nncn.bc2017.0132
BibTex Citekey: KlindtEEB2017
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Title: Bernstein Conference 2017
Place of Event: Berlin, Germany
Start-/End Date: 2017-09-13 - 2017-09-15

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Title: Bernstein Conference 2017
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: T 15 Start / End Page: 155 - 156 Identifier: -