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Evolving super stimuli for real neurons using deep generative networks

View ORCID ProfileCarlos R. Ponce, View ORCID ProfileWill Xiao, View ORCID ProfilePeter F. Schade, View ORCID ProfileTill S. Hartmann, View ORCID ProfileGabriel Kreiman, View ORCID ProfileMargaret S. Livingstone
doi: https://doi.org/10.1101/516484
Carlos R. Ponce
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
4Department of Neuroscience, Washington University School of Medicine, St. Louis MO 63110
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  • For correspondence: crponce@wustl.edu
Will Xiao
2Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138
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Peter F. Schade
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
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Till S. Hartmann
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
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Gabriel Kreiman
3Department of Ophthalmology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115
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Margaret S. Livingstone
1Department of Neurobiology, Harvard Medical School, Boston, MA 02115
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  • For correspondence: mlivingstone@hms.harvard.edu
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Abstract

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.

  • A generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.

  • The evolved images activated neurons more strongly than did thousands of natural images.

  • Distance in image space from the evolved images predicted responses of neurons to novel images.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 17, 2019.
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Evolving super stimuli for real neurons using deep generative networks
Carlos R. Ponce, Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, Margaret S. Livingstone
bioRxiv 516484; doi: https://doi.org/10.1101/516484
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Evolving super stimuli for real neurons using deep generative networks
Carlos R. Ponce, Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, Margaret S. Livingstone
bioRxiv 516484; doi: https://doi.org/10.1101/516484

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