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

Klindt, David A, Ecker, Alexander S, Euler, Thomas, Bethge, Matthias (2017) Neural system identification for large populations separating "what" and "where". In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA.


Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of "what" and "where". Learning deep convolutional feature spaces that are 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 readout 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 evaluate 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 of mouse primary visual cortex.

Item Type: Conference or Workshop Item (Paper)
Subjects: bioinformatics
neurobiology > neuroscience
neurobiology > neuroscience > systems neuroscience
CSHL Authors:
Communities: CSHL labs > Klindt lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 2017
Date Deposited: 11 Apr 2024 15:05
Last Modified: 11 Apr 2024 15:05

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